CN112320599B - Health monitoring method and system for port hoisting equipment - Google Patents
Health monitoring method and system for port hoisting equipment Download PDFInfo
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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
- B66C13/00—Other constructional features or details
- B66C13/16—Applications of indicating, registering, or weighing devices
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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
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Abstract
The invention discloses a health monitoring method and a system for port hoisting equipment, wherein the method comprises the following steps: acquiring real-time vibration data of port hoisting equipment; the real-time vibration data comprises motor vibration speed, motor vibration displacement, reducer vibration speed and reducer vibration displacement; preprocessing the real-time vibration data to obtain a speed root mean square value and a vibration speed peak-to-peak value of the real-time vibration data; and respectively comparing the speed root mean square value and the peak-to-peak value of the real-time vibration data with respective corresponding threshold values, and judging whether the port hoisting equipment has faults or not.
Description
Technical Field
The application relates to the technical field of port hoisting equipment, in particular to a health monitoring method and system for port hoisting equipment.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
In the field of port hoisting equipment, the safety of the equipment is crucial, and once sudden failure occurs, huge property loss and even casualties are likely to be caused. The damage of the equipment is mostly shown in the damage of the mechanism bearing, and the method for accurately predicting the service life of the bearing and the fault part has great research value.
The inventor finds that for bearing service life prediction, a support vector machine and a neural network are mostly used at present, the two methods need a large amount of data to carry out service life model training and then carry out service life prediction, are greatly influenced by the type and the working condition of a bearing, are complex and have low universality, are suitable for occasions with single type and little change of the working condition of the bearing, and are not suitable for the field of port hoisting equipment.
Disclosure of Invention
In order to overcome the defects of the prior art, the application provides a health monitoring method and system for port hoisting equipment; the bearing life can be accurately predicted, meanwhile, the fault position of the bearing can be detected, the overall health condition of the equipment is monitored, the influence of the type and the working condition of the bearing is small, and the bearing life prediction method is suitable for the field of port hoisting equipment.
In a first aspect, the application provides a health monitoring method for port hoisting equipment;
a health monitoring method for port hoisting equipment comprises the following steps:
acquiring real-time vibration data of port hoisting equipment; the real-time vibration data comprises motor vibration speed, motor vibration displacement, reducer vibration speed and reducer vibration displacement;
preprocessing the real-time vibration data to obtain a speed root mean square value and a vibration speed peak-to-peak value of the real-time vibration data;
and respectively comparing the speed root mean square value and the peak-to-peak value of the real-time vibration data with respective corresponding threshold values, and judging whether the port hoisting equipment fails.
In a second aspect, the application provides a health monitoring system for port crane equipment;
a port lifting equipment health monitoring system comprising:
an acquisition module configured to: acquiring real-time vibration data of port hoisting equipment; the real-time vibration data comprises motor vibration speed, motor vibration displacement, reducer vibration speed and reducer vibration displacement;
a pre-processing module configured to: preprocessing the real-time vibration data to obtain a speed root mean square value and a vibration speed peak-to-peak value of the real-time vibration data;
a fault detection module configured to: and respectively comparing the speed root mean square value and the peak-to-peak value of the real-time vibration data with respective corresponding threshold values, and judging whether the port hoisting equipment fails.
In a third aspect, the present application further provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein a processor is connected to the memory, the one or more computer programs being stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first aspect.
In a fourth aspect, the present application also provides a computer-readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
In a fifth aspect, the present application also provides a computer program (product) comprising a computer program for implementing the method of any of the preceding first aspects when run on one or more processors.
Compared with the prior art, the beneficial effect of this application is:
(1) The sensor collects vibration speed and vibration displacement data of the mechanism in operation, the vibration speed and the vibration displacement data are compared with a set threshold value after being processed, the damage condition of the equipment is judged, then a service life prediction function is solved, the residual service life of the equipment is calculated in real time, a vibration signal is subjected to time-frequency analysis and demodulation, and the damaged part is judged.
(2) The adaptability is strong, the service life judgment of the multi-model and multi-working-condition bearing is supported, and compared with a support vector machine and a neural network, a large amount of data storage is not needed.
(3) The judgment is accurate, the judgment of the mean sequence is introduced, the influence of the surrounding environment is small, and the noise signal can be effectively filtered.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the application, and the description of the exemplary embodiments and illustrations of the application are intended to explain the application and are not intended to limit the application.
FIG. 1 illustrates mounting positions of sensors of a hoisting mechanism;
FIG. 2 shows the mounting positions of the sensors of the carriage driving mechanism;
FIG. 3 illustrates the mounting location of the sensors of the pitch mechanism;
FIG. 4 is a flow chart of a method of data direct monitoring diagnostics;
FIG. 5 is a flow chart of a historical data comparison and life estimation method;
FIG. 6 is a flow chart of a fault site detection method.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure herein. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the invention may be combined with each other without conflict.
Example one
The embodiment provides a health monitoring method for port hoisting equipment;
as shown in fig. 4, a health monitoring method for a port crane includes:
s101: acquiring real-time vibration data of port hoisting equipment; the real-time vibration data comprises the vibration speed of the speed reducer and the vibration displacement of the speed reducer;
s102: preprocessing the real-time vibration data to obtain a speed root mean square value and a vibration speed peak-to-peak value of the real-time vibration data;
s103: and respectively comparing the speed root mean square value and the peak-to-peak value of the real-time vibration data with respective corresponding threshold values, and judging whether the port hoisting equipment fails.
Illustratively, the S101: acquiring real-time vibration data of port hoisting equipment; the real-time vibration data of the port hoisting equipment is obtained by using a sensor.
Illustratively, as shown in fig. 1, 2 and 3, the installation positions of the sensors include the following forms:
the sensors are arranged on a motor and a speed reducer of the hoisting mechanism;
the sensors are arranged on a motor and a speed reducer of the trolley driving mechanism;
the sensors are arranged on a motor and a speed reducer of the pitching mechanism.
Illustratively, the S101: acquiring real-time vibration data of port hoisting equipment; it is to read the vibration signal of the equipment and record the signal in classification.
The signal is read from the sensor, each mechanism data is independently stored and operated, after the mechanism motor is started, the recorded signal value is read every second until the mechanism motor stops running, and only the data from 3 seconds after the mechanism motor is started to 3 seconds before the mechanism motor stops is taken as effective data.
Illustratively, the S102: preprocessing the real-time vibration data to obtain a speed root mean square value of the real-time vibration data; the specific implementation mode comprises the following steps:
at regular intervals, respectively calculating the speed root mean square value of each group of data to form a speed root mean square value sequence, wherein the calculation formula is as follows:
wherein n is 1 ……n i And i is the number of the vibration data.
Illustratively, the S102: preprocessing the real-time vibration data to obtain a vibration speed peak value of the real-time vibration data; the specific implementation mode comprises the following steps:
respectively calculating the peak value of each group of data at regular intervals to form a peak-peak value sequence, wherein the calculation formula is as follows:
V pp ＝V max -V min
wherein, V max Is the maximum value of the vibration speed in one cycle, V min Is the minimum value of the vibration speed in one period.
Exemplary, S103: comparing the speed root mean square value and the vibration speed peak value of the real-time vibration data with respective corresponding threshold values respectively, and judging whether the port hoisting equipment has a fault; the specific implementation mode is as follows:
when the speed root mean square value of the real-time vibration data exceeds a set threshold value, an alarm is sent out;
and when the peak value of the vibration speed exceeds a set threshold value, giving an alarm.
As shown in fig. 5, for one or more embodiments, the method further comprises:
s104: preprocessing historical vibration data of port hoisting equipment to obtain a plurality of speed root mean square values; further acquiring the average value of a plurality of speed root mean square values; taking the average value of the root mean square values of a plurality of speeds as a historical judgment value; the historical judgment value is used for representing the average value of the vibration of each mechanism of the current port hoisting equipment in the normal running state;
s105: processing the preprocessed speed root mean square value by using a moving average method to obtain a new mean value sequence; the new mean sequence is used for representing the real-time running state of the port hoisting equipment;
s106: judging whether the new mean value sequence exceeds a historical judgment value or not; if so, an alarm is raised directly while the life budget is started.
It should be understood that the historical vibration data refers to the stable vibration data collected for at least one month after the equipment is disengaged from the running-in period, the vibration state of the equipment in the stable running process is represented, the historical judgment value is obtained through subsequent processing, and the historical judgment value is unchanged and is used as the judgment value for starting the life prediction program.
Illustratively, the S104: preprocessing historical vibration data of port hoisting equipment to obtain a plurality of speed root mean square values; further obtaining the average value of a plurality of speed root mean square values; taking the average value of the speed root-mean-square values as a historical judgment value; the historical judgment value is used for representing the average vibration value of the port hoisting equipment in the normal running state; the specific implementation form comprises:
solving a speed root mean square value sequence of the vibration signal;
when the speed root mean square value sequence reaches a set threshold value, the average value of a plurality of root mean square values is calculated to form a historical judgment value, the judgment value represents the vibration average value of the mechanism in the normal running state, and the collection time is controlled to be more than one month.
Illustratively, the S105: processing the preprocessed speed root mean square value by using a moving average method to obtain a new mean value sequence; the new mean sequence is used for representing the real-time running state of the port hoisting equipment; the specific implementation form is as follows:
the moving average method is used for processing the speed root-mean-square sequence, a root-mean-square average value is obtained every time a root-mean-square signal is calculated, the obtained average values form a new average value sequence, the sequence represents the stable vibration state of the equipment, the vibration noise can be effectively filtered, and the interference of the external environment on the running of the equipment is greatly reduced.
As one or more embodiments, the step of S106 performing lifetime budgeting specifically includes:
s1061: selecting a plurality of coordinate points from the new mean value sequence by an alternate sampling method;
s1062: establishing a life prediction function;
s1063: fitting the selected coordinate points into a life estimation function by using a function fitting method;
s1064: substituting the set bearing failure threshold value into a life prediction function to obtain the end time of the bearing life; comparing the service life ending time of the bearing with the current time to obtain the residual service life of the bearing;
s1065: repeating S1061-S1064 to obtain the residual life of the bearing corresponding to each speed root mean square value; and averaging all the residual lives of the bearings to obtain the average residual life.
Illustratively, the S1061: selecting a plurality of coordinate points from the new mean value sequence by an alternate sampling method; the specific implementation mode is as follows:
and selecting a plurality of coordinate points from the mean sequence by a point sampling method, wherein the number of the selected points is controlled to be more than 1000, and the time interval is controlled to be more than 0.1 hour.
Exemplary, S1062: establishing a life prediction function; the specific implementation mode is as follows:
wherein a1, b1, c1, a2, b2 and c2 are life coefficients and are obtained through fitting calculation; f (t) is a function value (y value); t is a time independent variable; e is a natural base number.
As shown in fig. 6, as one or more embodiments, the method further comprises:
s107: calculating the fault frequency of the corresponding bearing model; the failure frequency comprises: the fault frequency of the bearing inner ring, the fault frequency of the bearing outer ring, the fault frequency of the bearing retainer and the fault frequency of the bearing rolling body;
s108: performing wavelet packet analysis on the real-time vibration data to obtain multi-band frequency domain characteristics of the vibration signal;
s109: selecting frequency domain characteristics of the frequency band signals in the energy concentration by comparing the characteristic energy of each frequency band;
s110: envelope demodulation is carried out on the frequency domain characteristics of the energy concentrated frequency band signals to obtain the most original vibration frequency;
s111: and judging the fault position by comparing the most original vibration frequency with the fault frequency.
The bearing outer ring fault frequency is calculated according to the following formula:
wherein r is the bearing rotation speed, unit: rotating per minute; n is the number of bearing rolling bodies; d is the diameter of the rolling body; d is the pitch diameter of the bearing; and a is the contact angle of the rolling body.
The failure frequency of the bearing inner ring is calculated according to the following formula:
wherein r is the bearing rotation speed, unit: rotating per minute; n is the number of bearing rolling bodies; d is the diameter of the rolling body; d is the pitch diameter of the bearing; and a is the contact angle of the rolling body.
The failure frequency of the bearing rolling body is calculated according to the following formula:
wherein r is the bearing rotation speed, unit: rotating per minute; n is the number of bearing rolling bodies; d is the diameter of the rolling body; d is the pitch diameter of the bearing; and a is the contact angle of the rolling body.
The failure frequency of the bearing retainer is calculated according to the following formula:
wherein r is the bearing rotation speed, unit: rotating per minute; n is the number of bearing rolling bodies; d is the diameter of the rolling body; d is the pitch diameter of the bearing; and a is the contact angle of the rolling body.
Illustratively, the S108: performing wavelet packet analysis on the real-time vibration data to obtain multi-band frequency domain characteristics of the vibration signal; the specific implementation mode comprises the following steps:
carrying out wavelet packet decomposition on the original data, wherein a wavelet packet recursion formula is as follows:
wherein x is j,m (n) is the decomposition coefficient on the scale j.
After wavelet packet decomposition, the signal is divided into 2 n Each frequency segment is different in frequency.
Illustratively, the S109: selecting frequency domain characteristics of the frequency band signals in the energy concentration by comparing the characteristic energy of each frequency band; the specific implementation mode comprises the following steps:
calculating the energy ratio of each frequency section, namely calculating the ratio of the sum of the frequency amplitude of each section to the sum of all frequency amplitudes; and selecting the frequency section with the most concentrated energy as the frequency domain characteristic of the signal of the frequency section with the concentrated energy.
Illustratively, the S110: envelope demodulation is carried out on the frequency domain characteristics of the energy concentrated frequency band signals to obtain the most original vibration frequency; the specific implementation mode comprises the following steps:
when the fault frequency exists, the signal can be modulated, a high-frequency disordered signal is generated, fault judgment is seriously influenced, and the signal is demodulated by adopting envelope analysis at present to restore the original signal frequency.
The Hilbert transform method is adopted, and the formula is as follows:
the analytic signal z (t) of h (t) becomes:
z(t)＝h(t)+jH(t)＝a(t)e jθ(t)
the envelope signal a (t) becomes:
the enveloped signal is an original frequency and can obviously represent fault frequency.
Illustratively, the S111: judging the fault position by comparing the most original vibration frequency with the fault frequency; the method comprises the following specific steps:
and comparing the enveloped signal with a fault signal, and judging a fault part if the signal amplitude of the corresponding fault frequency in the enveloped signal sequence is far higher than the amplitudes of other signals.
Example two
The embodiment provides a health monitoring system for port hoisting equipment;
a port lifting equipment health monitoring system comprising:
an acquisition module configured to: acquiring real-time vibration data of port hoisting equipment; the real-time vibration data comprises motor vibration speed, motor vibration displacement, reducer vibration speed and reducer vibration displacement;
a pre-processing module configured to: preprocessing the real-time vibration data to obtain a speed root mean square value and a vibration speed peak-to-peak value of the real-time vibration data;
a fault detection module configured to: and respectively comparing the speed root mean square value and the peak-to-peak value of the real-time vibration data with respective corresponding threshold values, and judging whether the port hoisting equipment has faults or not.
It should be noted here that the acquiring module, the preprocessing module and the failure detecting module correspond to steps S101 to S103 in the first embodiment, and the modules are the same as the examples and application scenarios realized by the corresponding steps, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer executable instructions.
In the foregoing embodiments, the descriptions of the embodiments have different emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The proposed system can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the above modules is only one logical functional division, and in actual implementation, there may be another division, for example, multiple modules may be combined or integrated into another system, or some features may be omitted, or not executed.
EXAMPLE III
The present embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, a processor is connected with the memory, the one or more computer programs are stored in the memory, and when the electronic device runs, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first embodiment.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
The method in the first embodiment may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and combines hardware thereof to complete the steps of the method. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Example four
The present embodiments also provide a computer-readable storage medium for storing computer instructions, which when executed by a processor, perform the method of the first embodiment.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (7)
1. A health monitoring method for port hoisting equipment is characterized by comprising the following steps:
acquiring real-time vibration data of port hoisting equipment; the real-time vibration data comprises the vibration speed of the speed reducer and the vibration displacement of the speed reducer;
preprocessing the real-time vibration data to obtain a speed root mean square value and a vibration speed peak-to-peak value of the real-time vibration data;
comparing the speed root mean square value and the peak-to-peak value of the real-time vibration data with respective corresponding threshold values respectively, and judging whether the port hoisting equipment has faults or not;
preprocessing historical vibration data of port hoisting equipment to obtain a plurality of speed root mean square values; further obtaining the average value of a plurality of speed root mean square values; taking the average value of the speed root-mean-square values as a historical judgment value; the historical judgment value is used for representing the average value of the vibration of each mechanism of the current port hoisting equipment in the normal running state;
the method further comprises the following steps:
calculating the fault frequency of the corresponding bearing model; the failure frequency comprises: the bearing inner ring fault frequency, the bearing outer ring fault frequency, the bearing retainer fault frequency and the bearing rolling body fault frequency;
performing wavelet packet analysis on the real-time vibration data to obtain multi-band frequency domain characteristics of the vibration signal;
selecting frequency domain characteristics of the frequency band signals in the energy concentration by comparing the characteristic energy of each frequency band;
envelope demodulation is carried out on the frequency domain characteristics of the energy concentrated frequency band signals to obtain the most original vibration frequency;
judging the fault position by comparing the most original vibration frequency with the fault frequency;
selecting frequency domain characteristics of the frequency band signals in the energy concentration by comparing the characteristic energy of each frequency band; the specific implementation mode comprises the following steps:
calculating the energy ratio of each frequency section, namely calculating the ratio of the sum of the frequency amplitude of each section to the sum of all frequency amplitudes; selecting the frequency segment with the most concentrated energy as the frequency domain characteristic of the signal of the energy concentrated frequency band;
judging the fault position by comparing the most original vibration frequency with the fault frequency; the method comprises the following specific steps:
and comparing the enveloped signal with a fault signal, and judging a fault part if the signal amplitude of the corresponding fault frequency in the enveloped signal sequence is far higher than the amplitudes of other signals.
2. The method of claim 1, further comprising:
processing the preprocessed speed root mean square value by using a moving average method to obtain a new mean value sequence; the new mean sequence is used for representing the real-time running state of the port hoisting equipment;
judging whether the new mean value sequence exceeds a historical judgment value or not; if so, an alarm is raised directly while the life budget is started.
3. The method as claimed in claim 2, wherein the historical vibration data of the harbour crane is preprocessed to obtain a plurality of root mean square values of the speed; further obtaining the average value of a plurality of speed root mean square values; taking the average value of the speed root-mean-square values as a historical judgment value; the historical judgment value is used for representing the average value of the vibration of each mechanism of the current port hoisting equipment in the normal running state; the specific implementation form comprises:
solving a speed root mean square value sequence of the vibration signal;
when the speed root mean square value sequence reaches a set threshold value, the average value of a plurality of root mean square values is obtained to form a historical judgment value, and the judgment value represents the vibration average value of the mechanism in the normal running state.
4. The method of claim 2, wherein the preprocessed root mean square values of velocity are processed by a moving average method to obtain a new mean sequence; the new mean sequence is used for representing the real-time running state of the port hoisting equipment; the specific implementation form is as follows:
the moving average method is utilized to process the speed root-mean-square sequence, an average value of the root-mean-square is obtained every time a root-mean-square signal is calculated, the obtained average value forms a new average value sequence, the sequence represents the stable vibration state of the equipment, the vibration noise is effectively filtered, and the interference of the external environment to the running of the equipment is reduced.
5. The method of claim 2, wherein the step of performing a lifetime budget comprises:
selecting a plurality of coordinate points from the new mean value sequence by an alternate sampling method;
establishing a life prediction function;
fitting the selected coordinate points into a life estimation function by using a function fitting method;
substituting the set threshold value into a life prediction function to obtain the end time of the bearing life; comparing the service life ending time of the bearing with the current time to obtain the residual service life of the bearing;
repeating the steps to obtain the residual life of the bearing corresponding to each speed root mean square value; and averaging all the residual lives of the bearings to obtain the average residual life.
6. An electronic device, comprising: one or more processors, one or more memories, and one or more computer programs; wherein a processor is coupled to the memory, the one or more computer programs being stored in the memory, and wherein when the electronic device is running, the processor executes the one or more computer programs stored in the memory to cause the electronic device to perform the method of any of the preceding claims 1-5.
7. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 5.
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