CN112357771A - Ship-shore integrated equipment state monitoring system and method - Google Patents

Ship-shore integrated equipment state monitoring system and method Download PDF

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CN112357771A
CN112357771A CN202011301559.XA CN202011301559A CN112357771A CN 112357771 A CN112357771 A CN 112357771A CN 202011301559 A CN202011301559 A CN 202011301559A CN 112357771 A CN112357771 A CN 112357771A
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fault
data
fault diagnosis
stress
gantry crane
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CN112357771B (en
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王学兵
孙娇娇
蔡黄河
张健
闫广利
李洋
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Csic Qingdao Marine Equipment Research Institute Co ltd
Qingdao Haixi Heavy Duty Machinery Co Ltd
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Csic Qingdao Marine Equipment Research Institute Co ltd
Qingdao Haixi Heavy Duty Machinery Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
    • B66C15/00—Safety gear
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
    • B66C15/00—Safety gear
    • B66C15/06—Arrangements or use of warning devices
    • B66C15/065—Arrangements or use of warning devices electrical
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Testing And Monitoring For Control Systems (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

The invention provides a ship and shore integrated equipment state monitoring system and method, wherein the system comprises: the system comprises a stress strain sensor, a vibration temperature sensor, a tension sensor, an absolute value encoder, a current transformer, a relay switch and a plurality of data acquisition processors; the industrial personal computer is used for respectively monitoring stress data, vibration data, temperature data, lifting distance of a lifting mechanism, running distance of a lifting trolley, running distance of a crane, current data of a motor, relay contact switch data and door interlock switch data; analyzing the fault information by using a preset fault diagnosis knowledge base, and outputting a fault diagnosis result and a corresponding processing strategy; therefore, the industrial personal computer can monitor the real-time data of each device on line, and the working efficiency is prevented from being influenced; and fault diagnosis is carried out on corresponding equipment according to the operation data in the monitoring process, a fault diagnosis result is output, fault early warning is carried out, and the operation safety of the intelligent port is ensured.

Description

Ship-shore integrated equipment state monitoring system and method
Technical Field
The invention belongs to the technical field of intelligent port operation and maintenance support, and particularly relates to a ship-shore integrated equipment state monitoring system and method.
Background
The intelligent port carries the aspects of port entrance and exit management, loading and unloading management, warehousing management, centralized and distributed management and the like of daily goods.
In the operation process of an intelligent port, in order to ensure safe and efficient operation of the port, reduce economic loss as much as possible and avoid major accidents, the operation state of port equipment needs to be monitored.
In the prior art, periodical equipment maintenance and repair with fixed time is adopted, and off-line maintenance is needed, so that the production efficiency is reduced, and resource waste is caused; and the early warning can not be carried out on the sudden equipment failure, and the effect is limited.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a ship-shore integrated equipment state monitoring system and method, which are used for solving the problem that the production efficiency is reduced because manual off-line monitoring is needed when the running state of ship-shore integrated equipment is monitored in the prior art; and the technical problem that the operation safety of the intelligent port cannot be ensured because the early warning cannot be carried out on the sudden equipment failure.
The invention provides a ship and shore integrated equipment state monitoring system, which comprises:
the stress-strain sensor is used for acquiring stress data of a measuring point of the portal crane, and the measuring point comprises: legs, beam span, and beam 1/4 span;
the vibration temperature sensor is used for acquiring vibration data of motors and reduction boxes of the hoisting mechanism and the crane trolley and acquiring temperature data of a brake of the hoisting mechanism;
the tension sensor is used for collecting the weight of the lifting mechanism on the main hook and the auxiliary hook;
the absolute value encoder is used for acquiring the lifting distance of the lifting mechanism, the running distance of the trolley and the running distance of the gantry crane;
the current transformer is used for acquiring current data of the hoisting mechanism, the trolley and the gantry crane motor;
the relay switch is used for acquiring relay contact switch data and door interlocking switch data of the hoisting mechanism brake;
a plurality of data acquisition processors for correspondingly receiving and processing the stress data, the vibration data, the temperature data, the lifting distance of the lifting mechanism, the running distance of the trolley, the running distance of the gantry crane, the current data of the motor, the relay contact switch data and the door interlock switch data;
the industrial personal computer is used for monitoring the stress data, the vibration data, the temperature data, the lifting distance of the lifting mechanism, the running distance of the lifting trolley, the running distance of the portal crane, the current data of the motor, the relay contact switch data and the door interlocking switch data; and judging whether each data exceeds a corresponding threshold value in the monitoring process, if so, generating fault information based on the corresponding data, analyzing the fault information by using a preset fault diagnosis knowledge base, and outputting a fault diagnosis result and a corresponding processing strategy.
Optionally, before the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, the industrial personal computer is further configured to:
when the fault type is an electrical fault, acquiring a fault tree established for the electrical fault corresponding to each device;
for any one of the fault trees, converting the fault tree into at least one-dimensional fault branch;
and generating a corresponding fault diagnosis rule for the at least one-dimensional fault branch based on a fault diagnosis generation strategy in the fault diagnosis knowledge base to form a fault diagnosis rule set.
Optionally, before the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, the industrial personal computer is further configured to:
when the fault type is a mechanical fault, acquiring a first vibration signal of a target mechanical part of any equipment in a normal state and a second vibration signal of the target mechanical part in a fault state; the target machine component includes: a gear of the reduction gearbox;
decomposing the first vibration signal and the second vibration signal respectively to obtain corresponding first m components containing fault information;
extracting sample entropy characteristics from the first m components containing fault information, and constructing a fault characteristic value vector based on the sample entropy characteristics, wherein the fault characteristic value vector comprises a plurality of fault characteristic values, and each fault characteristic value has a corresponding mechanical fault category;
training the training sample by taking the fault characteristic value vector as a training sample based on a multivariate prediction model recognition algorithm to obtain a fault type prediction model; each fault characteristic value corresponds to a fault type prediction model.
Optionally, the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, including:
when the fault type of the fault information is determined to be an electrical fault, judging whether a fault event which can be successfully matched with the fault information exists in the fault diagnosis knowledge base or not;
if the fault event is the top event of the fault tree, judging whether the fault event is the top event of the fault tree, and if the fault event is not the top event, matching the fault event with a fault diagnosis rule set in a fault diagnosis knowledge base to obtain a successfully matched fault diagnosis rule subset;
analyzing the fault diagnosis rules in the fault diagnosis rule subset one by one based on a preset priority, sequentially obtaining fault occurrence reasons, and sending the fault occurrence reasons to a user;
and if the confirmation information sent by the user is received, determining that the fault diagnosis rule is established, and outputting a fault diagnosis result corresponding to the fault diagnosis rule and a corresponding processing strategy.
Optionally, the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, and after outputting a fault diagnosis result and a corresponding processing strategy, the industrial personal computer is further configured to:
processing the stress data in a preset time period to obtain effective stress data;
determining stress amplitude values and corresponding stress cycle times on corresponding measuring points of the gantry crane based on the effective stress data;
based on the formula
Figure BDA0002787065830000031
Determining a total damage value D of the gantry crane under each stress amplitude, wherein i is a fatigue load grade, and n isiFor the gantry crane to bear the total stress cycle times of stress amplitude values at all levels in one working cycle, NiThe stress cycle times which can be borne by the gantry crane under the independent action of each level of stress amplitude;
according to the formula D + t0·Dt＝KtDetermining an accumulated damage value K of the gantry cranetSaid t is0Length of service of the gantry crane, DtThe damage value of the gantry crane is the daily damage value;
according to the formula
Figure BDA0002787065830000041
Determining the remaining service life t of the gantry craner。
The invention also provides a ship-shore integrated equipment state detection method, which comprises the following steps:
stress data of a measuring point of the portal crane are acquired by using a stress-strain sensor, wherein the measuring point comprises: legs, beam span, and beam 1/4 span; collecting vibration data of motors and reduction boxes of a hoisting mechanism and a crane trolley and collecting temperature data of a brake of the hoisting mechanism by using a vibration temperature sensor; acquiring the weight of the hoisting mechanism on the main hook and the auxiliary hook by using a tension sensor; acquiring the lifting distance of the lifting mechanism, the running distance of the trolley and the running distance of the gantry crane by using an absolute value encoder; collecting current data of the hoisting mechanism, the crane trolley and the gantry crane motor by using a current transformer; acquiring relay contact switch data and door interlock switch data of the hoisting mechanism brake by using a relay switch;
correspondingly receiving and processing the stress data, the vibration data, the temperature data, the lifting distance of the lifting mechanism, the running distance of the lifting trolley, the running distance of the portal crane, the current data of the motor, the relay contact switch data and the door interlocking switch data by utilizing a plurality of data acquisition processors;
monitoring the stress data, the vibration data, the temperature data, the lifting distance of the lifting mechanism, the running distance of the hoisting trolley, the running distance of the gantry crane, the current data of the motor, the relay contact switch data and the door interlocking switch data by using an industrial personal computer; and judging whether each data exceeds a corresponding threshold value in the monitoring process, if so, generating fault information based on the corresponding data, analyzing the fault information by using a preset fault diagnosis knowledge base, and outputting a fault diagnosis result and a corresponding processing strategy.
Optionally, before analyzing the fault information by using a preset fault diagnosis knowledge base and outputting a fault diagnosis result and a corresponding processing strategy, the method further includes:
when the fault type is an electrical fault, acquiring a fault tree established for the electrical fault corresponding to each device;
for any one of the fault trees, converting the fault tree into at least one-dimensional fault branch;
and generating a corresponding fault diagnosis rule for the at least one-dimensional fault branch based on a fault diagnosis generation strategy in the fault diagnosis knowledge base to form a fault diagnosis rule set.
Optionally, before analyzing the fault information by using a preset fault diagnosis knowledge base and outputting a fault diagnosis result and a corresponding processing strategy, the method further includes:
when the fault type is a mechanical fault, acquiring a first vibration signal of a target mechanical part of any equipment in a normal state and a second vibration signal of the target mechanical part in a fault state; the target machine component includes: a gear of the reduction gearbox;
decomposing the first vibration signal and the second vibration signal respectively to obtain corresponding first m components containing fault information;
extracting sample entropy characteristics from the first m components containing fault information, and constructing a fault characteristic value vector based on the sample entropy characteristics, wherein the fault characteristic value vector comprises a plurality of fault characteristic values, and each fault characteristic value has a corresponding mechanical fault category;
training the training sample by taking the fault characteristic value vector as a training sample based on a multivariate prediction model recognition algorithm to obtain a fault type prediction model; each fault characteristic value corresponds to a fault type prediction model.
Optionally, the analyzing the fault information by using a preset fault diagnosis knowledge base includes:
when the fault type of the fault information is determined to be an electrical fault, judging whether a fault event which can be successfully matched with the fault information exists in the fault diagnosis knowledge base or not;
if the fault event is the top event of the fault tree, judging whether the fault event is the top event of the fault tree, and if the fault event is not the top event, matching the fault event with a fault diagnosis rule set in a fault diagnosis knowledge base to obtain a successfully matched fault diagnosis rule subset;
analyzing the fault diagnosis rules in the fault diagnosis rule subset one by one based on a preset priority, sequentially obtaining fault occurrence reasons, and sending the fault occurrence reasons to a user;
and if the confirmation information sent by the user is received, determining that the fault diagnosis rule is established, and outputting a fault diagnosis result corresponding to the fault diagnosis rule and a corresponding processing strategy.
Optionally, after analyzing the fault information by using a preset fault diagnosis knowledge base, the method further includes:
processing the stress data in a preset time period to obtain effective stress data;
determining stress amplitude values and corresponding stress cycle times on corresponding measuring points of the gantry crane based on the effective stress data;
based on the formula
Figure BDA0002787065830000061
Determining a total damage value D of the gantry crane under each stress amplitude, wherein i is a fatigue load grade, and n isiFor the gantry crane to bear the total stress cycle times of stress amplitude values at all levels in one working cycle, NiThe stress cycle times which can be borne by the gantry crane under the independent action of each level of stress amplitude;
according to the formula D + t0·Dt＝KtDetermining an accumulated damage value K of the gantry cranetSaid t is0Length of service of the gantry crane, DtThe damage value of the gantry crane is the daily damage value;
according to the formula
Figure BDA0002787065830000062
Determining the remaining service life t of the gantry craner。
The invention provides a ship and shore integrated equipment state monitoring system and method, wherein the system comprises: the stress-strain sensor is used for acquiring stress data of a measuring point of the portal crane, and the measuring point comprises: legs, beam span, and beam 1/4 span; the vibration temperature sensor is used for acquiring vibration data of motors and reduction boxes of the hoisting mechanism and the crane trolley and acquiring temperature data of a brake of the hoisting mechanism; the tension sensor is used for collecting the weight of the lifting mechanism on the main hook and the auxiliary hook; the absolute value encoder is used for acquiring the lifting distance of the lifting mechanism, the running distance of the trolley and the running distance of the gantry crane; the current transformer is used for acquiring current data of the hoisting mechanism, the trolley and the gantry crane motor; the relay switch is used for acquiring relay contact switch data and door interlocking switch data of the hoisting mechanism brake; a plurality of data acquisition processors for correspondingly receiving and processing the stress data, the vibration data, the temperature data, the lifting distance of the lifting mechanism, the running distance of the trolley, the running distance of the gantry crane, the current data of the motor, the relay contact switch data and the door interlock switch data; the industrial personal computer is used for monitoring the stress data, the vibration data, the temperature data, the lifting distance of the lifting mechanism, the running distance of the lifting trolley, the running distance of the portal crane, the current data of the motor, the relay contact switch data and the door interlocking switch data; judging whether each data exceeds a corresponding threshold value in the monitoring process, if so, generating fault information based on the corresponding data, analyzing the fault information by using a preset fault diagnosis knowledge base, and outputting a fault diagnosis result and a corresponding processing strategy; therefore, the real-time operation data of each device is acquired by using the corresponding sensor, and the industrial personal computer can monitor the real-time operation data of each device on line in the actual production process, so that the working efficiency is prevented from being influenced; and in the monitoring process, fault diagnosis can be performed on corresponding equipment according to the operation data, a fault diagnosis result is output, fault early warning is performed, workers are reminded of paying attention to potential safety hazards of the corresponding equipment, and operation safety of the intelligent port is guaranteed.
Drawings
Fig. 1 is a schematic structural diagram of a ship-shore integrated equipment state monitoring system provided in an embodiment of the present invention;
fig. 2 is an overall structural schematic diagram of a fault tree of a hoisting mechanism provided in an embodiment of the present invention;
fig. 3 is a schematic flow chart of a ship-shore integrated equipment state monitoring method provided in the embodiment of the present invention.
Detailed Description
The problem that in the prior art, when the running state of ship-shore integrated equipment is monitored, manual off-line monitoring is needed, so that the production efficiency is reduced is solved; the invention provides a ship and shore integrated equipment state monitoring system and method, and solves the technical problems that sudden equipment failure cannot be pre-warned, and further the operation safety of an intelligent port cannot be ensured.
The technical solution of the present invention is further described in detail by the accompanying drawings and the specific embodiments.
This embodiment provides a ship and bank integration equipment state monitoring system, ship and bank integration equipment mainly including: large mechanical equipment such as gantry cranes, crane trolleys, hoisting mechanisms, ship unloaders and the like; that is, the present embodiment mainly monitors the state of the large mechanical equipment. As shown in fig. 1, the system includes: the system comprises a stress strain sensor 1, a vibration temperature sensor 2, a tension sensor 3, an absolute value encoder 4, a current transformer 5, a relay switch 6, a data acquisition processor 7 and an industrial personal computer 8; wherein the data acquisition processor 7 comprises a plurality of sensors, one for each sensor.
The stress-strain sensor 1 is arranged at the measuring points of the portal crane and is used for acquiring the stress-strain data of each measuring point; the measuring points comprise: the legs, the beam span, and the beam 1/4 span of the gantry crane.
In actual operation, the hoisting mechanism and the crane trolley run frequently, so that vibration data of motors and reduction boxes of the hoisting mechanism and the crane trolley and temperature data of a brake of the hoisting mechanism are acquired by using the vibration temperature sensor 2. Wherein, the vibration temperature sensor 2 is respectively arranged on the hoisting mechanism, the motor and the reduction gearbox of the crane trolley and the brake of the hoisting mechanism.
The tension sensor 3 is installed in the hoisting mechanism and used for collecting the weight of the load on the main hook and the auxiliary hook of the hoisting mechanism. The absolute value encoder 4 is arranged on the hoisting mechanism, the trolley and the portal crane and is used for acquiring the hoisting distance of the hoisting mechanism, the running distance of the trolley and the running distance of the portal crane; the current transformer is used for acquiring current data and voltage data of a hoisting mechanism, a trolley and a gantry crane motor; and the relay switch is used for acquiring relay contact switch data and door interlocking switch data of the hoisting mechanism brake. It is to be understood that the weight data, the distance data, the travel distance, the current data, the relay contact switch data, and the door interlock switch data are state quantity data.
Specifically, the stress-strain sensor 1 adopts a strain-electrometric method for stress monitoring, when a corresponding measuring point of the gantry crane deforms, the resistance value of the sensor changes correspondingly, then the resistance change is converted into the change of voltage (or current) through a resistance-strain gauge, and then the change of voltage (or current) is converted into a strain value or a signal of voltage (or current) which is in direct proportion to the strain value is output, so that the measured strain or stress can be obtained. The stress-strain sensor in this embodiment has a precision of ± 1.0% FS and a sensitivity of 1.0 μ ∈, and therefore, the measurement precision can be ensured.
After the stress strain sensor 1 acquires corresponding stress strain data, the application strain data is sent to the corresponding data acquisition unit 7 through the intelligent transmission module. The network protocol adopted by the intelligent transmission module is an IEEE Std 802.15.4 standard protocol, and the solar monocrystalline silicon solar cell is adopted for supplying power, so that the environment is protected, and the continuous work of the equipment can be ensured without maintenance. Under the condition of ensuring the non-shielding ideal environment, the device can be installed nearby the stress strain sensor and connected with the sensor cable to complete networking, and the farthest wireless transmission distance can reach 600m, so that the using amount of instrument cables and network communication cables and the cable laying engineering amount are greatly reduced, and the production cost and the workload are reduced.
The vibration temperature sensor 2 is a piezoelectric acceleration sensor, has good signal quality, low noise, strong external interference resistance and long-distance measurement, and is adsorbed on the corresponding motor and the reduction gearbox through the mounting bracket.
It should be noted that, in order to remind the staff of the vibration fault in time at the production site, the system further includes: the vibration sensor node is used for monitoring data on site, and when the vibration data exceed a preset vibration threshold value, a pre-mapped LED indicating lamp (with a radio function) can be lightened to prompt a worker that vibration hidden dangers occur. Wherein, the vibration sensor node adopts a molded reinforced thermoplastic polyester shell, an annular sealed transparent sealing cover, a molded acrylic lens and stainless steel hardware, and can bear 1200psi impact. A lithium battery is arranged in the vibration sensor node for supplying power, and the wireless communication distance of the vibration sensor node is 1 km.
When the vibration temperature sensor 2 acquires the vibration data and the temperature data, the vibration data and the temperature data are transmitted to the data acquisition processor 7.
And the tension sensor 3 is used for measuring the weight of the load on the main hook and the auxiliary hook of the hoisting mechanism. In the embodiment, the stress change of the steel wire rope when the heavy object is lifted is mainly measured through the tension sensor, and the steel wire rope is pulled. The force acts on the tension sensor through the guide wheel, the tension sensor generates micro deformation after being stressed to cause internal resistance change of a strain gauge inside the tension sensor, the change is changed into 4-20 mA current which is linear with the tension force after passing through a circuit of an internal transmitter of the tension force, then the corresponding tension force is determined through the current, and then the weight of a lifted heavy object is determined through the tension force.
Similarly, when the tension sensor 3 collects the tension data, the data is transmitted to the corresponding data collection processor 7.
The absolute value encoder 4 is generally installed on a main shaft of a motor of equipment to be monitored (a hoisting mechanism, a trolley and a gantry crane) and is used for acquiring the hoisting distance of the hoisting mechanism, the running distance of the trolley and the running distance of the gantry crane. The absolute value encoder is a calibrated absolute value encoder, and the running distance of the equipment to be monitored is determined mainly according to the distance which is converted from the diameter of a rotating shaft of the equipment to be monitored to the distance which is passed by the rotating shaft when the rotating shaft rotates for one circle.
The relay switch 5 is mainly used for measuring the motor braking states and the interlocking protection states of the hoisting mechanism, the gantry crane and the crane trolley, and the current transformer 6 is mainly used for collecting current data and voltage data of the hoisting mechanism, the crane trolley and the gantry crane motor. Here, in the actual operation process, the trolley, the hoisting mechanism and the gantry crane need to be matched with each other to complete the operation, and the interlocking protection state means that when one of the trolley, the hoisting mechanism and the gantry crane is executing the action, the other two devices need to be kept in a static state, so that potential safety hazards are avoided.
Similarly, when the relay switch 5 and the current transformer 6 acquire corresponding data, the data are transmitted to the data acquisition processor 7.
Further, since the crane is usually operated in a port, a dock, etc. with a complicated environment, the external environment of the crane usually affects the normal operation of the crane and even causes an accident. For example, an external obstacle or other people entering the crane operation area by mistake is very easy to cause accidents under the condition that the operator does not find the obstacle. Therefore, in this embodiment, the system further includes: and the image acquisition equipment is arranged at the working place of the crane and is used for capturing working environment data around the crane and sending the environment data to the corresponding data acquisition processor 7. The image acquisition equipment can be a camera, and the camera has the functions of dynamic change capture and night infrared shooting. In order to monitor the surrounding environment of the crane in 360 degrees without dead angles, six cameras are installed in the embodiment.
When the corresponding data acquisition processor 7 receives the data sent by the corresponding sensor, the stress data, the vibration data, the temperature data, the lifting distance of the lifting mechanism, the running distance of the crane trolley, the running distance of the gantry crane, the current data of the motor, the relay contact switch data and the door interlock switch data are processed, and then the data are sent to the industrial personal computer 8 through the corresponding hot spot relay (such as a wireless network bridge).
Here, in this embodiment, a mode combining wireless communication and wired communication is adopted, a wireless network bridge is installed on a device to be tested for wireless transmission, each wireless network bridge has a terminal and a hotspot relay function, and when a hotspot relay fails, hotspot relays of other devices can be used for data transmission. The wired local area network is mainly used for networking between the server and the client, and is convenient for remote monitoring of the client.
The industrial personal computer 8 is used for monitoring stress data, the vibration data, temperature data, the lifting distance of the lifting mechanism, the running distance of the crane trolley, the running distance of the portal crane, current data of the motor, relay contact switch data and door interlocking switch data after receiving the data; and judging whether each data exceeds a corresponding threshold value in the monitoring process, if so, generating fault information based on the corresponding data, analyzing the fault information by using a preset fault diagnosis knowledge base, and outputting a fault diagnosis result and a corresponding processing strategy.
Here, the industrial personal computer 8 may create multiple threads to enable communication with the different data acquisition processors 7, transmitting data through socket network communication, the network transmission being based on the TCP protocol. Specifically, after the industrial personal computer 8 and the data acquisition processor 7 are connected through a network, data exchange is performed between the industrial personal computer 8 and the data acquisition processor 7, so that in order to avoid disconnection of the network due to unstable network connection, during data exchange, the industrial personal computer 8 sends a heartbeat data packet to the data acquisition processor 7 at intervals of a preset first time period and waits for response of the data acquisition processor 7; if the industrial personal computer does not receive the response within the preset second time length, the network connection is disconnected by default, a reconnection data frame is sent to the data acquisition processor 7 at the moment, and after the response frame sent by the data acquisition processor 7 is received, a starting frame is sent to the data acquisition processor 7, and data transmission is restarted.
When the industrial personal computer 8 receives the data, the data packet is analyzed based on the self-defined protocol, the data in the data packet is extracted and displayed on an interface in real time, meanwhile, the industrial personal computer opens up a data storage thread, the received data are stored in real time, the data are stored in a local database in a file form, and the storage life is generally one month for workers to inquire. And the industrial personal computer 8 can send each data to the server 9, and long-term storage is carried out on the data, so that the working personnel can conveniently inquire the historical data.
Further, the industrial personal computer 8 can perform fault analysis processing on each received data, and perform intelligent fault diagnosis and health state prediction. Since the fault types generally include two categories, namely electrical faults and mechanical faults, the present embodiment has different fault analysis strategies for different fault types. Generally, for an electrical fault, a fault diagnosis knowledge base can be used for carrying out fault analysis on the electrical fault; and aiming at the mechanical fault, a fault prediction model can be established for fault diagnosis by analyzing the vibration data.
As an alternative embodiment, before the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, the industrial personal computer is further configured to:
when the fault type is an electrical fault, acquiring a fault tree established for the electrical fault corresponding to each device; the apparatus comprises: the gantry crane, the hoisting mechanism and the crane trolley;
for any fault tree, converting the fault tree into at least one-dimensional fault branch;
and generating a corresponding fault diagnosis rule for at least one-dimensional fault branch based on a fault diagnosis generation strategy in the fault diagnosis knowledge base to form a fault diagnosis rule set.
For example, the hoisting mechanism may be referred to as an electrical fault tree of the hoisting mechanism in fig. 2, where the top event in fig. 2 is a fault of the hoisting mechanism, the rectangular frame in fig. 2 is a specific fault result, and the circular frame is a fault cause, so that the fault result and the fault cause may form a plurality of one-dimensional fault branches. As can be seen from fig. 2, a hoist fault may include three types: abnormal steel wire rope, brake and lifting action; after the fault tree is converted, for example, the one-dimensional fault branch corresponding to the abnormal steel wire rope may include: wire rope abnormality-wire rope breakage, wire rope abnormality-wire rope run-out disorder on drum-no-load start error and wire rope abnormality-wire rope run-out disorder on drum-rope guide fault. The one-dimensional fault branch with abnormal brake and abnormal lifting action can be converted according to the same conversion mode, and the description is omitted here.
And after the one-dimensional fault branches are determined, generating a corresponding fault diagnosis rule for at least one-dimensional fault branch based on a fault diagnosis generation strategy in a fault diagnosis knowledge base to form a fault diagnosis rule set.
Here, the fault node in the fault tree can be judged according to the monitored corresponding data; for example, whether the wire rope is broken or not can be judged by judging whether the tension data of the tension sensor exceeds a threshold value or not.
As an optional embodiment, before the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, the industrial personal computer is further configured to:
when the fault type is a mechanical fault, acquiring a first vibration signal of a target mechanical part in a normal state and a second vibration signal of the target mechanical part in a fault state aiming at the target mechanical part of any equipment; the target machine component includes: a gear of the reduction gearbox;
respectively decomposing the first vibration signal and the second vibration signal to obtain corresponding first m components containing fault information;
extracting sample entropy characteristics from the previous m components containing fault information, and constructing a fault characteristic value vector based on the sample entropy characteristics, wherein the fault characteristic value vector comprises a plurality of fault characteristic values, and each fault characteristic value has a corresponding mechanical fault category; m is an integer greater than 1;
training the training samples by taking the fault characteristic value vector as a training sample based on a multivariate prediction model recognition algorithm to obtain a fault type prediction model; each fault characteristic value corresponds to a fault type prediction model.
After the fault diagnosis rule set and the fault type prediction model are determined, as an optional embodiment, the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, including:
when the fault type of the fault information is determined to be an electrical fault, judging whether a fault event which can be successfully matched with the fault information exists in a fault diagnosis knowledge base or not;
if a fault event which can be successfully matched with the fault information exists, judging whether the fault event is a top event of the fault tree, if not, matching the fault event with a fault diagnosis rule set in a fault diagnosis knowledge base to obtain a successfully matched fault diagnosis rule subset; the fault diagnosis rule subset which is successfully matched may contain a plurality of fault diagnosis rules;
analyzing the fault diagnosis rules in the fault diagnosis rule subset one by one based on a preset priority, sequentially obtaining fault occurrence reasons, and sending the fault reasons to a user;
and if the confirmation information sent by the user is received, determining that the fault diagnosis rule is established, and outputting a fault diagnosis result corresponding to the fault diagnosis rule and a corresponding processing strategy.
And for any current fault diagnosis rule, deleting the current fault diagnosis rule from the fault diagnosis rule subset if the negative information sent by the user is received.
As an optional embodiment, if the fault event is a top event of a fault tree, retrieving a minimum cut set K causing the fault event, judging whether the minimum cut set is empty, if not, analyzing fault diagnosis rules in the minimum cut set one by one based on a preset priority, sequentially obtaining fault occurrence reasons, and sending the fault reasons to a user; and if the confirmation information sent by the user is received, determining that the fault diagnosis rule is established, and outputting a fault diagnosis result corresponding to the fault diagnosis rule and a corresponding processing strategy. And if the negative information sent by the user is received, deleting the fault diagnosis rule in the minimum cut set.
Here, the priority of the fault diagnosis rule is determined according to the importance of each fault node of the fault tree, and the importance of the fault node can be determined through qualitative analysis of the fault tree. When the fault tree is analyzed qualitatively, all fault reason combinations which can cause the occurrence of the fault tree top event are determined through a gate structure function.
As an optional embodiment, the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, including:
and when the fault type of the fault information is determined to be gear fault, converting the vibration data into a target characteristic vector, predicting the target characteristic vector by using a corresponding fault prediction model, and outputting a fault aiming result and a corresponding processing measure of the gear.
Furthermore, because the cyclic load born by the structural parts is a continuous and random process in the working process of the large mechanical equipment, the health of the gantry crane can be predicted according to the stress data.
As an optional embodiment, the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, and after outputting a fault diagnosis result and a corresponding processing strategy, the industrial personal computer is further configured to:
processing the internal stress data in a preset time period to obtain effective stress data;
determining stress amplitude values and corresponding stress cycle times on corresponding measuring points of the gantry crane based on the effective stress data;
based on the formula
Figure BDA0002787065830000141
Determining the total damage value D, i of the gantry crane under each stress amplitude as the fatigue load grade, niFor the gantry crane to bear the total stress cycle times of stress amplitude values at all levels in one working cycle, NiFor gantry cranesThe stress cycle times that the machine can bear under the independent action of each level of stress amplitude;
according to the formula D + t0·Dt＝KtDetermining an accumulated damage value K of a gantry cranet，t0Length of service for gantry cranes, DtThe damage value of the gantry crane every day;
according to the formula
Figure BDA0002787065830000142
Determining the remaining service life t of a gantry craner。
Therefore, the residual service life of the gantry crane can be accurately predicted, and the operation safety is ensured.
Wherein, handle the internal stress data of preset time quantum, obtain effective stress data, include:
and compressing the equivalent stress data, processing the peak-valley values and removing the invalid stress amplitude values.
Specifically, only one stress data with continuously equal factor values can be reserved, so that the equivalent stress data needs to be compressed; extracting and removing the peak-valley value in the time period, so as to conveniently remove the invalid amplitude value; since the influence of the stress amplitude having a relatively small value on fatigue is small, the stress amplitude having a relatively small value can be regarded as an ineffective stress amplitude. After removal, effective stress data (effective stress amplitude) is obtained.
And then determining the stress amplitude and the corresponding stress cycle number of each measuring point of the gantry crane based on simulation software by using a rain flow counting principle.
As an optional embodiment, in the operation process, the server 9 may monitor the connection request of the remote client 10 all the time, and if the connection request sent by the remote client 10 is monitored, the server 9 connects with the remote client 10 and forwards the real-time data to the remote client 10.
The client 10 may be understood as a remote monitoring end, and a human-computer interaction interface is provided on the remote client, so that a user can log in and view the monitoring interfaces of the devices on a login interface of the client. The monitoring interface includes: the system comprises a vibration state monitoring interface of the gantry crane, wherein vibration data are displayed on the monitoring interface in a oscillogram mode; when the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base and determines a fault diagnosis result and a corresponding processing strategy, the fault diagnosis result and the corresponding processing strategy can be directly sent to the client, or the fault diagnosis result and the corresponding processing strategy can be forwarded to the client 10 through the server 9, that is, when a peak value exceeds a set corresponding threshold value, a monitoring interface starts to flash and alarm to prompt a worker.
The stress monitoring interface of the gantry crane comprises a measuring point arrangement schematic diagram and stress values of measuring points.
Crane, hoisting mechanism and trolley each state quantity monitoring interface: weight, lifting height and running distance are all directly displayed by numerical values, and similarly, if the state quantity exceeds a preset corresponding threshold value, an alarm is triggered to prompt a worker.
A video monitoring interface: the field working environment can be checked through the video monitoring interface, and the historical video data backtracking function interface can provide functions of historical file query, playing, fast forwarding, downloading and the like.
Therefore, the system provided by the embodiment can monitor the running state of the equipment in real time, can also perform fault early warning, and can remind workers in time to avoid potential safety hazards; and this implementation still provides the video monitoring function, and the operation environment of supervisory equipment further improves the operation safety.
Based on the same inventive concept, the application also provides a ship-shore integrated equipment state monitoring method, which is detailed in the second embodiment.
Example two
The embodiment provides a ship and shore integrated equipment state monitoring method, as shown in fig. 3, the method includes:
s310, collecting stress data of a measuring point of the portal crane by using a stress strain sensor, wherein the measuring point comprises: legs, beam span, and beam 1/4 span; collecting vibration data of motors and reduction boxes of a hoisting mechanism and a crane trolley and collecting temperature data of a brake of the hoisting mechanism by using a vibration temperature sensor; acquiring the weight of the hoisting mechanism on the main hook and the auxiliary hook by using a tension sensor; acquiring the lifting distance of the lifting mechanism, the running distance of the trolley and the running distance of the gantry crane by using an absolute value encoder; collecting current data of the hoisting mechanism, the crane trolley and the gantry crane motor by using a current transformer; acquiring relay contact switch data and door interlock switch data of the hoisting mechanism brake by using a relay switch;
s311, utilizing a plurality of data acquisition processors to correspondingly receive and process the stress data, the vibration data, the temperature data, the lifting distance of the lifting mechanism, the running distance of the crane trolley, the running distance of the portal crane, the current data of the motor, the relay contact switch data and the door interlocking switch data;
s312, monitoring the stress data, the vibration data, the temperature data, the lifting distance of the lifting mechanism, the running distance of the crane trolley, the running distance of the gantry crane, the current data of the motor, the relay contact switch data and the door interlocking switch data by using an industrial personal computer; and judging whether each data exceeds a corresponding threshold value in the monitoring process, if so, generating fault information based on the corresponding data, analyzing the fault information by using a preset fault diagnosis knowledge base, and outputting a fault diagnosis result and a corresponding processing strategy.
Specifically, the stress-strain sensor is arranged at the measuring points of the portal crane and used for collecting the stress-strain data of each measuring point, because the stress-strain data mainly aims at the steel structure; the measuring points comprise: the legs, the beam span, and the beam 1/4 span of the gantry crane.
In actual operation, the hoisting mechanism and the crane trolley run frequently, so vibration data of motors and reduction boxes of the hoisting mechanism and the crane trolley are collected by using vibration temperature sensors, and temperature data of a brake of the hoisting mechanism are collected. The vibration temperature sensors are respectively arranged on the hoisting mechanism, a motor and a reduction gearbox of the crane trolley and a brake of the hoisting mechanism.
The tension sensor is arranged in the lifting mechanism and used for collecting the weight of loads carried on the main hook and the auxiliary hook of the lifting mechanism. The absolute value encoder is arranged on the hoisting mechanism, the trolley and the portal crane and is used for acquiring the hoisting distance of the hoisting mechanism, the running distance of the trolley and the running distance of the portal crane; the current transformer is used for acquiring current data and voltage data of a hoisting mechanism, a trolley and a gantry crane motor; and the relay switch is used for acquiring relay contact switch data and door interlocking switch data of the hoisting mechanism brake. It is to be understood that the weight data, the distance data, the travel distance, the current data, the relay contact switch data, and the door interlock switch data are state quantity data.
Specifically, the stress-strain sensor adopts a strain electrical measurement method for stress monitoring, when a corresponding measuring point of the gantry crane deforms, the resistance value of the sensor changes correspondingly, then the resistance change is converted into the change of voltage (or current) through a resistance strain gauge, and then the change of voltage (or current) is converted into a strain value or a signal of voltage (or current) which is in direct proportion to the strain value is output, so that the measured strain or stress can be obtained. The stress-strain sensor in this embodiment has a precision of ± 1.0% FS and a sensitivity of 1.0 μ ∈, and therefore, the measurement precision can be ensured.
After the stress-strain sensor collects corresponding stress-strain data, the application strain data are sent to the corresponding data collector through the intelligent transmission module. The network protocol adopted by the intelligent transmission module is an IEEE Std 802.15.4 standard protocol, and the solar monocrystalline silicon solar cell is adopted for supplying power, so that the environment is protected, and the continuous work of the equipment can be ensured without maintenance. Under the condition of ensuring the non-shielding ideal environment, the device can be installed nearby the stress strain sensor and connected with the sensor cable to complete networking, and the farthest wireless transmission distance can reach 600m, so that the using amount of instrument cables and network communication cables and the cable laying engineering amount are greatly reduced, and the production cost and the workload are reduced.
The vibration temperature sensor is a piezoelectric acceleration sensor, has good signal quality, low noise, strong external interference resistance and long-distance measurement, and is adsorbed on the corresponding motor and the reduction gearbox through the mounting bracket.
It should be noted that, in order to remind the staff of the vibration fault in time at the production site, the system further includes: the vibration sensor node is used for monitoring data on site, and when the vibration data exceed a preset vibration threshold value, a pre-mapped LED indicating lamp (with a radio function) can be lightened to prompt a worker that vibration hidden dangers occur. Wherein, the vibration sensor node adopts a molded reinforced thermoplastic polyester shell, an annular sealed transparent sealing cover, a molded acrylic lens and stainless steel hardware, and can bear 1200psi impact. A lithium battery is arranged in the vibration sensor node for supplying power, and the wireless communication distance of the vibration sensor node is 1 km.
When the vibration temperature sensor acquires the vibration data and the temperature data, the vibration data and the temperature data are transmitted to the data acquisition processor 7.
And the tension sensor 3 is used for measuring the weight of the load on the main hook and the auxiliary hook of the hoisting mechanism. In the embodiment, the stress change of the steel wire rope when the heavy object is lifted is mainly measured through the tension sensor, and the steel wire rope is pulled. The force acts on the tension sensor through the guide wheel, the tension sensor generates micro deformation after being stressed to cause internal resistance change of a strain gauge inside the tension sensor, the change is changed into 4-20 mA current which is linear with the tension force after passing through a circuit of an internal transmitter of the tension force, then the corresponding tension force is determined through the current, and then the weight of a lifted heavy object is determined through the tension force.
Similarly, when the tension sensor 3 collects the tension data, the data is transmitted to the corresponding data collection processor 7.
The absolute value encoder 4 is generally installed on a main shaft of a motor of equipment to be monitored (a hoisting mechanism, a trolley and a gantry crane) and is used for acquiring the hoisting distance of the hoisting mechanism, the running distance of the trolley and the running distance of the gantry crane. The absolute value encoder is a calibrated absolute value encoder, and the running distance of the equipment to be monitored is determined mainly according to the distance which is converted from the diameter of a rotating shaft of the equipment to be monitored to the distance which is passed by the rotating shaft when the rotating shaft rotates for one circle.
The relay switch 5 is mainly used for measuring the motor braking states and the interlocking protection states of the hoisting mechanism, the gantry crane and the crane trolley, and the current transformer 6 is mainly used for collecting current data and voltage data of the hoisting mechanism, the crane trolley and the gantry crane motor. Here, in the actual operation process, the trolley, the hoisting mechanism and the gantry crane need to be matched with each other to complete the operation, and the interlocking protection state means that when one of the trolley, the hoisting mechanism and the gantry crane is executing the action, the other two devices need to be kept in a static state, so that potential safety hazards are avoided.
Similarly, after the relay switch and the current transformer acquire corresponding data, the data are transmitted to the data acquisition processor.
Further, since the crane is usually operated in a port, a dock, etc. with a complicated environment, the external environment of the crane usually affects the normal operation of the crane and even causes an accident. For example, an external obstacle or other people entering the crane operation area by mistake is very easy to cause accidents under the condition that the operator does not find the obstacle. Therefore, in this embodiment, the system further includes: the image acquisition equipment is arranged at the working place of the crane and used for capturing working environment data around the crane and sending the environment data to the corresponding data acquisition processor. The image acquisition equipment can be a camera, and the camera has the functions of dynamic change capture and night infrared shooting. In order to monitor the surrounding environment of the crane in 360 degrees without dead angles, six cameras are installed in the embodiment.
When the corresponding data acquisition processor receives the data sent by the corresponding sensor, the stress data, the vibration data, the temperature data, the lifting distance of the lifting mechanism, the running distance of the crane trolley, the running distance of the gantry crane, the current data of the motor, the relay contact switch data and the door interlock switch data are processed, and then the data are sent to the industrial personal computer 8 through the corresponding hot spot relay (such as a wireless network bridge).
Here, in this embodiment, a mode combining wireless communication and wired communication is adopted, a wireless network bridge is installed on a device to be tested for wireless transmission, each wireless network bridge has a terminal and a hotspot relay function, and when a hotspot relay fails, hotspot relays of other devices can be used for data transmission. The wired local area network is mainly used for networking between the server and the client, and is convenient for remote monitoring of the client.
After receiving the data, the industrial personal computer is used for respectively monitoring stress data, the vibration data, temperature data, the lifting distance of the lifting mechanism, the running distance of the crane trolley, the running distance of the portal crane, current data of the motor, relay contact switch data and door interlocking switch data; and judging whether each data exceeds a corresponding threshold value in the monitoring process, if so, generating fault information based on the corresponding data, analyzing the fault information by using a preset fault diagnosis knowledge base, and outputting a fault diagnosis result and a corresponding processing strategy.
Here, the industrial personal computer may create multiple threads to enable communication with different data acquisition processors, transmit data through socket network communication, the network transmission being based on the TCP protocol. Specifically, after network connection is established between the industrial personal computer 8 and the data acquisition processor, data exchange is performed between the industrial personal computer and the data acquisition processor, so that in order to avoid disconnection of the network connection due to unstable network connection, during data exchange, the industrial personal computer sends a heartbeat data packet to the data acquisition processor every preset first time interval and waits for response of the data acquisition processor; if the industrial personal computer does not receive the response within the preset second time length, the network connection is disconnected by default, a reconnection data frame is sent to the data acquisition processor at the moment, and after the response frame sent by the data acquisition processor is received, a starting frame is sent to the data acquisition processor to restart data transmission.
When the industrial personal computer receives the data, the data packet is analyzed based on the self-defined protocol, the data in the data packet is extracted and displayed on an interface in real time, meanwhile, the industrial personal computer opens up a data storage thread and stores the received data in real time, the data is stored in a local database in a file form, and the storage life is generally one month for workers to inquire. And moreover, the industrial control machine can send each data to the server to store the data for a long time, so that the workers can conveniently inquire the historical data.
Furthermore, the industrial personal computer can analyze and process the received data to perform intelligent fault diagnosis and health state prediction. Since the fault types generally include two categories, namely electrical faults and mechanical faults, the present embodiment has different fault analysis strategies for different fault types. Generally, for an electrical fault, a fault diagnosis knowledge base can be used for carrying out fault analysis on the electrical fault; and aiming at the mechanical fault, a fault prediction model can be established for fault diagnosis by analyzing the vibration data.
As an alternative embodiment, before the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, the industrial personal computer is further configured to:
when the fault type is an electrical fault, acquiring a fault tree established for the electrical fault corresponding to each device; the apparatus comprises: the gantry crane, the hoisting mechanism and the crane trolley;
for any fault tree, converting the fault tree into at least one-dimensional fault branch;
and generating a corresponding fault diagnosis rule for at least one-dimensional fault branch based on a fault diagnosis generation strategy in the fault diagnosis knowledge base to form a fault diagnosis rule set.
For example, the hoisting mechanism may be referred to as an electrical fault tree of the hoisting mechanism in fig. 2, where the top event in fig. 2 is a fault of the hoisting mechanism, the rectangular frame in fig. 2 is a specific fault result, and the circular frame is a fault cause, so that the fault result and the fault cause may form a plurality of one-dimensional fault branches. As can be seen from fig. 2, a hoist fault may include three types: abnormal steel wire rope, brake and lifting action; after the fault tree is converted, for example, the one-dimensional fault branch corresponding to the abnormal steel wire rope may include: wire rope abnormality-wire rope breakage, wire rope abnormality-wire rope run-out disorder on drum-no-load start error and wire rope abnormality-wire rope run-out disorder on drum-rope guide fault. The one-dimensional fault branch with abnormal brake and abnormal lifting action can be converted according to the same conversion mode, and the description is omitted here.
And after the one-dimensional fault branches are determined, generating a corresponding fault diagnosis rule for at least one-dimensional fault branch based on a fault diagnosis generation strategy in a fault diagnosis knowledge base to form a fault diagnosis rule set.
Here, the fault node in the fault tree can be judged according to the monitored corresponding data; for example, whether the wire rope is broken or not can be judged by judging whether the tension data of the tension sensor exceeds a threshold value or not.
As an optional embodiment, before the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, the industrial personal computer is further configured to:
when the fault type is a mechanical fault, acquiring a first vibration signal of a target mechanical part in a normal state and a second vibration signal of the target mechanical part in a fault state aiming at the target mechanical part of any equipment; the target machine component includes: a gear of the reduction gearbox;
respectively decomposing the first vibration signal and the second vibration signal to obtain corresponding first m components containing fault information;
extracting sample entropy characteristics from the previous m components containing fault information, and constructing a fault characteristic value vector based on the sample entropy characteristics, wherein the fault characteristic value vector comprises a plurality of fault characteristic values, and each fault characteristic value has a corresponding mechanical fault category;
training the training samples by taking the fault characteristic value vector as a training sample based on a multivariate prediction model recognition algorithm to obtain a fault type prediction model; each fault characteristic value corresponds to a fault type prediction model.
After the fault diagnosis rule set and the fault type prediction model are determined, as an optional embodiment, the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, including:
when the fault type of the fault information is determined to be an electrical fault, judging whether a fault event which can be successfully matched with the fault information exists in a fault diagnosis knowledge base or not;
if a fault event which can be successfully matched with the fault information exists, judging whether the fault event is a top event of the fault tree, if not, matching the fault event with a fault diagnosis rule set in a fault diagnosis knowledge base to obtain a successfully matched fault diagnosis rule subset; the fault diagnosis rule subset which is successfully matched may contain a plurality of fault diagnosis rules;
analyzing the fault diagnosis rules in the fault diagnosis rule subset one by one based on a preset priority, sequentially obtaining fault occurrence reasons, and sending the fault reasons to a user;
and if the confirmation information sent by the user is received, determining that the fault diagnosis rule is established, and outputting a fault diagnosis result corresponding to the fault diagnosis rule and a corresponding processing strategy.
And for any current fault diagnosis rule, deleting the current fault diagnosis rule from the fault diagnosis rule subset if the negative information sent by the user is received.
As an optional embodiment, if the fault event is a top event of a fault tree, retrieving a minimum cut set K causing the fault event, judging whether the minimum cut set is empty, if not, analyzing fault diagnosis rules in the minimum cut set one by one based on a preset priority, sequentially obtaining fault occurrence reasons, and sending the fault reasons to a user; and if the confirmation information sent by the user is received, determining that the fault diagnosis rule is established, and outputting a fault diagnosis result corresponding to the fault diagnosis rule and a corresponding processing strategy. And if the negative information sent by the user is received, deleting the fault diagnosis rule in the minimum cut set.
Here, the priority of the fault diagnosis rule is determined according to the importance of each fault node of the fault tree, and the importance of the fault node can be determined through qualitative analysis of the fault tree. When the fault tree is analyzed qualitatively, all fault reason combinations which can cause the occurrence of the fault tree top event are determined through a gate structure function.
As an optional embodiment, the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, including:
and when the fault type of the fault information is determined to be gear fault, converting the vibration data into a target characteristic vector, predicting the target characteristic vector by using a corresponding fault prediction model, and outputting a fault aiming result and a corresponding processing measure of the gear.
Furthermore, because the cyclic load born by the structural parts is a continuous and random process in the working process of the large mechanical equipment, the health of the gantry crane can be predicted according to the stress data.
As an optional embodiment, the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, and after outputting a fault diagnosis result and a corresponding processing strategy, the industrial personal computer is further configured to:
processing the internal stress data in a preset time period to obtain effective stress data;
determining stress amplitude values and corresponding stress cycle times on corresponding measuring points of the gantry crane based on the effective stress data;
based on the formula
Figure BDA0002787065830000221
Determining the total damage value D, i of the gantry crane under each stress amplitude as the fatigue load grade, niFor the gantry crane to bear the total stress cycle times of stress amplitude values at all levels in one working cycle, NiThe stress cycle times which can be borne by the portal crane under the independent action of each level of stress amplitude;
according to the formula D + t0·Dt＝KtDetermining an accumulated damage value K of a gantry cranet，t0Length of service for gantry cranes, DtThe damage value of the gantry crane every day;
according to the formula
Figure BDA0002787065830000231
Determining the remaining service life t of a gantry craner。
Therefore, the residual service life of the gantry crane can be accurately predicted, and the operation safety is ensured.
Wherein, handle the internal stress data of preset time quantum, obtain effective stress data, include:
and compressing the equivalent stress data, processing the peak-valley values and removing the invalid stress amplitude values.
Specifically, only one stress data with continuously equal factor values can be reserved, so that the equivalent stress data needs to be compressed; extracting and removing the peak-valley value in the time period, so as to conveniently remove the invalid amplitude value; since the influence of the stress amplitude having a relatively small value on fatigue is small, the stress amplitude having a relatively small value can be regarded as an ineffective stress amplitude. After removal, effective stress data (effective stress amplitude) is obtained.
And then determining the stress amplitude and the corresponding stress cycle number of each measuring point of the gantry crane based on simulation software by using a rain flow counting principle.
As an optional embodiment, in the operation process, the server may monitor the connection request of the remote client (user), and if the connection request sent by the remote client is monitored, the server is connected with the remote client, and forwards the real-time data to the remote client.
The client can be understood as a remote monitoring end, a human-computer interaction interface is arranged on the remote client, and a user can log in on a login interface of the client and check monitoring interfaces of all devices. The monitoring interface includes: the system comprises a vibration state monitoring interface of the gantry crane, wherein vibration data are displayed on the monitoring interface in a oscillogram mode; when the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base and determines a fault diagnosis result and a corresponding processing strategy, the fault diagnosis result and the corresponding processing strategy can be directly sent to the client side, or the fault diagnosis result and the corresponding processing strategy can be forwarded to the client side through the server, namely when a peak value exceeds a set corresponding threshold value, a monitoring interface starts to flash and alarm to prompt a worker.
The stress monitoring interface of the gantry crane comprises a measuring point arrangement schematic diagram and stress values of measuring points.
Crane, hoisting mechanism and trolley each state quantity monitoring interface: weight, lifting height and running distance are all directly displayed by numerical values, and similarly, if the state quantity exceeds a preset corresponding threshold value, an alarm is triggered to prompt a worker.
A video monitoring interface: the field working environment can be checked through the video monitoring interface, and the historical video data backtracking function interface can provide functions of historical file query, playing, fast forwarding, downloading and the like.
Therefore, the system provided by the embodiment can monitor the running state of the equipment in real time, can also perform fault early warning, and can remind workers in time to avoid potential safety hazards; and this implementation still provides the video monitoring function, and the operation environment of supervisory equipment further improves the operation safety.
The ship-shore integrated equipment state monitoring system and method provided by the invention have the beneficial effects that at least:
the invention provides a ship and shore integrated equipment state monitoring system and method, wherein the system comprises: the stress-strain sensor is used for acquiring stress data of a measuring point of the portal crane, and the measuring point comprises: legs, beam span, and beam 1/4 span; the vibration temperature sensor is used for acquiring vibration data of motors and reduction boxes of the hoisting mechanism and the crane trolley and acquiring temperature data of a brake of the hoisting mechanism; the tension sensor is used for collecting the weight of the lifting mechanism on the main hook and the auxiliary hook; the absolute value encoder is used for acquiring the lifting distance of the lifting mechanism, the running distance of the trolley and the running distance of the gantry crane; the current transformer is used for acquiring current data of the hoisting mechanism, the trolley and the gantry crane motor; the relay switch is used for acquiring relay contact switch data and door interlocking switch data of the hoisting mechanism brake; a plurality of data acquisition processors for correspondingly receiving and processing the stress data, the vibration data, the temperature data, the lifting distance of the lifting mechanism, the running distance of the trolley, the running distance of the gantry crane, the current data of the motor, the relay contact switch data and the door interlock switch data; the industrial personal computer is used for monitoring the stress data, the vibration data, the temperature data, the lifting distance of the lifting mechanism, the running distance of the lifting trolley, the running distance of the portal crane, the current data of the motor, the relay contact switch data and the door interlocking switch data; judging whether each data exceeds a corresponding threshold value in the monitoring process, if so, generating fault information based on the corresponding data, analyzing the fault information by using a preset fault diagnosis knowledge base, and outputting a fault diagnosis result and a corresponding processing strategy; therefore, the real-time operation data of each device is acquired by using the corresponding sensor, and the industrial personal computer can monitor the real-time operation data of each device on line in the actual production process, so that the working efficiency is prevented from being influenced; and in the monitoring process, fault diagnosis can be performed on corresponding equipment according to the operation data, a fault diagnosis result is output, fault early warning is performed, workers are reminded of paying attention to potential safety hazards of the corresponding equipment, and operation safety of the intelligent port is guaranteed.
The above description is only exemplary of the present invention and should not be taken as limiting the scope of the present invention, and any modifications, equivalents, improvements, etc. that are within the spirit and principle of the present invention should be included in the present invention.

Claims (10)

1. A ship-shore integrated equipment condition monitoring system, the system comprising:
the stress-strain sensor is used for acquiring stress data of a measuring point of the portal crane, and the measuring point comprises: legs, beam span, and beam 1/4 span;
the vibration temperature sensor is used for acquiring vibration data of motors and reduction boxes of the hoisting mechanism and the crane trolley and acquiring temperature data of a brake of the hoisting mechanism;
the tension sensor is used for collecting the weight of the lifting mechanism on the main hook and the auxiliary hook;
the absolute value encoder is used for acquiring the lifting distance of the lifting mechanism, the running distance of the trolley and the running distance of the gantry crane;
the current transformer is used for acquiring current data of the hoisting mechanism, the trolley and the gantry crane motor;
the relay switch is used for acquiring relay contact switch data and door interlocking switch data of the hoisting mechanism brake;
a plurality of data acquisition processors for correspondingly receiving and processing the stress data, the vibration data, the temperature data, the lifting distance of the lifting mechanism, the running distance of the trolley, the running distance of the gantry crane, the current data of the motor, the relay contact switch data and the door interlock switch data;
the industrial personal computer is used for monitoring the stress data, the vibration data, the temperature data, the lifting distance of the lifting mechanism, the running distance of the lifting trolley, the running distance of the portal crane, the current data of the motor, the relay contact switch data and the door interlocking switch data; and judging whether each data exceeds a corresponding threshold value in the monitoring process, if so, generating fault information based on the corresponding data, analyzing the fault information by using a preset fault diagnosis knowledge base, and outputting a fault diagnosis result and a corresponding processing strategy.
2. The system of claim 1, wherein before the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, the industrial personal computer is further configured to:
when the fault type is an electrical fault, acquiring a fault tree established for the electrical fault corresponding to each device;
for any one of the fault trees, converting the fault tree into at least one-dimensional fault branch;
and generating a corresponding fault diagnosis rule for the at least one-dimensional fault branch based on a fault diagnosis generation strategy in the fault diagnosis knowledge base to form a fault diagnosis rule set.
3. The system of claim 1, wherein before the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, the industrial personal computer is further configured to:
when the fault type is a mechanical fault, acquiring a first vibration signal of a target mechanical part of any equipment in a normal state and a second vibration signal of the target mechanical part in a fault state; the target machine component includes: a gear of the reduction gearbox;
decomposing the first vibration signal and the second vibration signal respectively to obtain corresponding first m components containing fault information;
extracting sample entropy characteristics from the first m components containing fault information, and constructing a fault characteristic value vector based on the sample entropy characteristics, wherein the fault characteristic value vector comprises a plurality of fault characteristic values, and each fault characteristic value has a corresponding mechanical fault category;
training the training sample by taking the fault characteristic value vector as a training sample based on a multivariate prediction model recognition algorithm to obtain a fault type prediction model; each fault characteristic value corresponds to a fault type prediction model.
4. The system of claim 1, wherein the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, and the method comprises the following steps:
when the fault type of the fault information is determined to be an electrical fault, judging whether a fault event which can be successfully matched with the fault information exists in the fault diagnosis knowledge base or not;
if the fault event is the top event of the fault tree, judging whether the fault event is the top event of the fault tree, and if the fault event is not the top event, matching the fault event with a fault diagnosis rule set in a fault diagnosis knowledge base to obtain a successfully matched fault diagnosis rule subset;
analyzing the fault diagnosis rules in the fault diagnosis rule subset one by one based on a preset priority, sequentially obtaining fault occurrence reasons, and sending the fault occurrence reasons to a user;
and if the confirmation information sent by the user is received, determining that the fault diagnosis rule is established, and outputting a fault diagnosis result corresponding to the fault diagnosis rule and a corresponding processing strategy.
5. The system of claim 1, wherein the industrial personal computer analyzes the fault information by using a preset fault diagnosis knowledge base, and after outputting a fault diagnosis result and a corresponding processing strategy, is further configured to:
processing the stress data in a preset time period to obtain effective stress data;
determining stress amplitude values and corresponding stress cycle times on corresponding measuring points of the gantry crane based on the effective stress data;
based on the formula
Figure FDA0002787065820000031
Determining a total damage value D of the gantry crane under each stress amplitude, wherein i is a fatigue load grade, and n isiFor the gantry crane to bear the total stress cycle times of stress amplitude values at all levels in one working cycle, NiThe stress cycle times which can be borne by the gantry crane under the independent action of each level of stress amplitude;
according to the formula D + t0·Dt＝KtDetermining an accumulated damage value K of the gantry cranetSaid t is0Length of service of the gantry crane, DtThe damage value of the gantry crane is the daily damage value;
according to the formula
Figure FDA0002787065820000032
Determining the remaining service life t of the gantry craner。
6. A ship-shore integrated equipment state detection method is characterized by comprising the following steps:
stress data of a measuring point of the portal crane are acquired by using a stress-strain sensor, wherein the measuring point comprises: legs, beam span, and beam 1/4 span; collecting vibration data of motors and reduction boxes of a hoisting mechanism and a crane trolley and collecting temperature data of a brake of the hoisting mechanism by using a vibration temperature sensor; acquiring the weight of the hoisting mechanism on the main hook and the auxiliary hook by using a tension sensor; acquiring the lifting distance of the lifting mechanism, the running distance of the trolley and the running distance of the gantry crane by using an absolute value encoder; collecting current data of the hoisting mechanism, the crane trolley and the gantry crane motor by using a current transformer; acquiring relay contact switch data and door interlock switch data of the hoisting mechanism brake by using a relay switch;
correspondingly receiving and processing the stress data, the vibration data, the temperature data, the lifting distance of the lifting mechanism, the running distance of the lifting trolley, the running distance of the portal crane, the current data of the motor, the relay contact switch data and the door interlocking switch data by utilizing a plurality of data acquisition processors;
monitoring the stress data, the vibration data, the temperature data, the lifting distance of the lifting mechanism, the running distance of the hoisting trolley, the running distance of the gantry crane, the current data of the motor, the relay contact switch data and the door interlocking switch data by using an industrial personal computer; and judging whether each data exceeds a corresponding threshold value in the monitoring process, if so, generating fault information based on the corresponding data, analyzing the fault information by using a preset fault diagnosis knowledge base, and outputting a fault diagnosis result and a corresponding processing strategy.
7. The method of claim 6, wherein before analyzing the fault information by using a preset fault diagnosis knowledge base and outputting a fault diagnosis result and a corresponding processing strategy, the method further comprises:
when the fault type is an electrical fault, acquiring a fault tree established for the electrical fault corresponding to each device;
for any one of the fault trees, converting the fault tree into at least one-dimensional fault branch;
and generating a corresponding fault diagnosis rule for the at least one-dimensional fault branch based on a fault diagnosis generation strategy in the fault diagnosis knowledge base to form a fault diagnosis rule set.
8. The method of claim 6, wherein before analyzing the fault information by using a preset fault diagnosis knowledge base and outputting a fault diagnosis result and a corresponding processing strategy, the method further comprises:
when the fault type is a mechanical fault, acquiring a first vibration signal of a target mechanical part of any equipment in a normal state and a second vibration signal of the target mechanical part in a fault state; the target machine component includes: a gear of the reduction gearbox;
decomposing the first vibration signal and the second vibration signal respectively to obtain corresponding first m components containing fault information;
extracting sample entropy characteristics from the first m components containing fault information, and constructing a fault characteristic value vector based on the sample entropy characteristics, wherein the fault characteristic value vector comprises a plurality of fault characteristic values, and each fault characteristic value has a corresponding mechanical fault category;
training the training sample by taking the fault characteristic value vector as a training sample based on a multivariate prediction model recognition algorithm to obtain a fault type prediction model; each fault characteristic value corresponds to a fault type prediction model.
9. The method of claim 6, wherein analyzing the fault information using a predetermined fault diagnosis knowledge base comprises:
when the fault type of the fault information is determined to be an electrical fault, judging whether a fault event which can be successfully matched with the fault information exists in the fault diagnosis knowledge base or not;
if the fault event is the top event of the fault tree, judging whether the fault event is the top event of the fault tree, and if the fault event is not the top event, matching the fault event with a fault diagnosis rule set in a fault diagnosis knowledge base to obtain a successfully matched fault diagnosis rule subset;
analyzing the fault diagnosis rules in the fault diagnosis rule subset one by one based on a preset priority, sequentially obtaining fault occurrence reasons, and sending the fault occurrence reasons to a user;
and if the confirmation information sent by the user is received, determining that the fault diagnosis rule is established, and outputting a fault diagnosis result corresponding to the fault diagnosis rule and a corresponding processing strategy.
10. The method of claim 6, wherein after analyzing the fault information using a predetermined fault diagnosis knowledge base, further comprising:
processing the stress data in a preset time period to obtain effective stress data;
determining stress amplitude values and corresponding stress cycle times on corresponding measuring points of the gantry crane based on the effective stress data;
based on the formula
Figure FDA0002787065820000051
Determining a total damage value D of the gantry crane under each stress amplitude, wherein i is a fatigue load grade, and n isiFor the gantry crane to bear the total stress cycle times of stress amplitude values at all levels in one working cycle, NiThe stress cycle times which can be borne by the gantry crane under the independent action of each level of stress amplitude;
according to the formula D + t0·Dt＝KtDetermining an accumulated damage value K of the gantry cranetSaid t is0Length of service of the gantry crane, DtThe damage value of the gantry crane is the daily damage value;
according to the formula
Figure FDA0002787065820000061
Determining the remaining service life t of the gantry craner。
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