CN113703371A - Device and method for detecting equipment fault - Google Patents

Device and method for detecting equipment fault Download PDF

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Publication number
CN113703371A
CN113703371A CN202111022718.7A CN202111022718A CN113703371A CN 113703371 A CN113703371 A CN 113703371A CN 202111022718 A CN202111022718 A CN 202111022718A CN 113703371 A CN113703371 A CN 113703371A
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fault
equipment
frequency
data
vibration
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陈威宁
苏俭博
洪永生
武晓举
刘文飞
陈其伟
秦小龙
田慕琴
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Shanxi Huakongweiye Technology Co ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G05B19/0423—Input/output
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/24—Pc safety
    • G05B2219/24215—Scada supervisory control and data acquisition

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Abstract

The invention discloses a device and a method for detecting equipment faults, which belong to the technical field related to fault detection and specifically comprise the following steps: collecting data: collecting various signal data of each collection point of the equipment; and (3) data analysis: analyzing the frequency spectrum of the signal data according to the fault type to obtain a characteristic component; and (3) calculating and outputting: calculating an output value of the neural network by taking the characteristic component as an input of the neural network; and (3) fault judgment: and judging the state of the equipment according to the output value. The intelligent coal dressing equipment realizes the intellectualization of the daily routing inspection of the equipment by comprehensively sensing the coal dressing equipment and utilizing advanced signal processing and artificial intelligence technology, improves the accuracy of analysis and diagnosis and ensures the safe operation of the equipment. And by selecting the embedded microprocessor and using the battery as a power supply, the device for detecting the equipment fault is further miniaturized and lightened, the labor intensity of inspection personnel is reduced, and the inspection efficiency of the equipment is improved.

Description

Device and method for detecting equipment fault
Technical Field
The invention relates to the technical field related to fault detection, in particular to a device and a method for detecting equipment faults.
Background
The safety of the equipment of the coal preparation plant is vital to the coal preparation production, the routine inspection of the equipment is an essential link for ensuring the normal operation of the equipment, but the coal preparation process is a highly automatic process, the equipment has many types and large quantity, the manual inspection is mainly used at present, the found problems are manually recorded, the workload is high, the errors are easy to occur, the information which can be obtained through the senses of people is very limited, the obvious faults can be seen or sensed, and the early prevention can not be achieved through the inspection of the unobvious or weak faults. In addition, the physical quantities of the inspection cannot be effectively recorded, analyzed and stored. Therefore, it is an urgent need to solve the problem of developing a portable and highly intelligent detection device.
Disclosure of Invention
In view of this, the present invention provides a device and a method for detecting a device failure.
In order to achieve the above purpose, the invention provides the following technical scheme:
a method for detecting equipment faults comprises the following specific steps:
collecting data: collecting various signal data of each collection point of the equipment;
and (3) data analysis: analyzing the frequency spectrum of the signal data according to the fault type to obtain a characteristic component;
and (3) calculating and outputting: calculating an output value of the neural network by taking the characteristic component as an input of the neural network;
and (3) fault judgment: and judging the state of the equipment according to the output value.
Preferably, the detection device is self-checked before data acquisition.
Preferably, the fault types comprise a rotor broken bar fault, a stator core and stator coil loosening fault, an air gap dynamic eccentric fault, a rolling element diameter inconsistency fault of a rolling bearing, a bearing nonlinear vibration fault and an air gap eccentric fault.
Preferably, the frequency of the characteristic component of the rotor bar break fault in the frequency spectrum of the stator wire current signal is as follows:
fb＝(1±2ks)f1 (1)；
in the formula: f. ofbCharacteristic frequency of rotor broken bar fault in frequency spectrum of stator line current signal; f. of1Is the power supply frequency; s is slip; k is a positive integer.
Preferably, the frequency of the characteristic component of the rotor broken bar fault in the vibration signal frequency spectrum is as follows:
Figure BDA0003242113020000021
in the formula: f. ofbzCharacteristic frequency of rotor broken bar fault in vibration signal frequency spectrum; f. of1Is the power supply frequency; s is slip; r ═ 1, ± 2, ± 3 … ± n; p is the number of pole pairs.
Preferably, the frequency of the characteristic component of the eccentric fault in the stator current signal frequency spectrum is:
fec＝f1±fr (3)；
in the formula (I), the compound is shown in the specification,
Figure BDA0003242113020000022
is the motor speed frequency; s is slip; p is the number of pole pairs.
Preferably, the eccentricity fault is characterized in the vibration signal spectrum by: generated at 1/2sf1Periodically pulsating electromagnetic vibrations.
Preferably, the characteristic frequency of the vibration signal in the bearing nonlinear vibration fault is an integral multiple or a fraction multiple of the rotation frequency.
An apparatus for detecting a device failure, comprising: the temperature measuring device comprises an embedded microcomputer, a vibration and current collecting device, a sound collecting device, a temperature measuring device and an environment temperature and humidity collecting device, wherein the sound collecting device, the temperature measuring device and the environment temperature and humidity collecting device are all connected with the embedded microcomputer; the vibration and current acquisition device is connected with the embedded microcomputer through an acquisition card.
Preferably, the system also comprises a battery data acquisition device, and the battery data acquisition device is connected with the embedded microcomputer.
According to the technical scheme, compared with the prior art, the invention discloses the device and the method for detecting the equipment fault, the intellectualization of the daily routing inspection of the equipment is realized by comprehensively sensing the coal preparation equipment and utilizing advanced signal processing and artificial intelligence technology, the accuracy of analysis and diagnosis is improved, and the safe operation of the equipment is ensured. And by selecting the embedded microprocessor and using the battery as a power supply, the device for detecting the equipment fault is further miniaturized and lightened, the labor intensity of inspection personnel is reduced, and the inspection efficiency of the equipment is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic flow chart of the steps of the present invention;
fig. 2 is a schematic structural diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention discloses a device and a method for detecting equipment faults, as shown in figure 1, the device and the method specifically comprise a device and a method for detecting equipment faults, in a coal preparation plant, large-scale equipment mainly comprises an asynchronous motor, a vibrating screen and the like, the embodiment is based on a data driving method, data signals of vibration, current, sound, temperature, environment temperature and humidity and the like of the equipment are respectively collected, and as the temperature and the humidity are signals with slow change, only a real-time value is obtained, the vibration, the current and the sound are high-frequency signals, and the operation condition of the equipment can be given through spectrum analysis, so that the method for processing the vibration, the current and the sound signals comprises the following steps:
collecting information: scanning the equipment code, then contacting the sensor with the acquisition point of the equipment (namely the key point marked on the equipment) in sequence, and reading back the information by a background program of a microcomputer.
And (3) data analysis: the wavelet packet analysis is carried out on the vibration and current signals of different points of the equipment to obtain characteristic components, and the method specifically comprises the following steps:
carrying out 9-layer wavelet packet decomposition on the current signal, and selecting S3(29Hz-39Hz)、S4(39Hz-49Hz)、S5(49Hz-59Hz)、S6(59Hz-68Hz)、S10(98Hz-108Hz)、S15(147Hz-157Hz)、S16(157Hz-166Hz)、S17(166Hz-176Hz)、S22(215Hz-225Hz)、S34(333Hz-342Hz) is used as a wavelet packet energy characteristic value for judging the squirrel cage broken bar fault; selecting wavelet packet energy of 4 frequency bands of 5 times, 7 times, 11 times and 13 times of the fundamental frequency of the motor stator current, namely: s25(245Hz-254Hz)、S35(342Hz-352Hz)、S56(548Hz-558Hz)、S66(646Hz-656Hz) as a characteristic value of the motor air gap eccentric fault; selecting S4(39Hz-49Hz)、S9(88Hz-98Hz)、S10(98Hz-108Hz)、S16(157Hz-166Hz) is taken as a motor air gap dynamic eccentric fault characteristic value.
8 layers of wavelet packet decomposition is carried out on the radial vibration signals, and S is selected5(118Hz-138Hz)、S10(196Hz-215Hz)、S15(294Hz-313Hz)、S20(393Hz-412Hz) is used as a wavelet packet energy characteristic value for judging the loosening fault of the stator core and the stator coil; the air gap dynamic eccentric fault is characterized in that electromagnetic vibration of rotor rotation frequency and rotation field synchronous rotation speed frequency can occur, and the electromagnetic vibration is 1/2sf1In order to make the period of the motor pulsate, the motor often generates electromagnetic noise consistent with the pulse beat, so S is taken5(118Hz-138Hz)、S6(118Hz-138Hz) is taken as an air gap dynamic eccentric fault characteristic frequency band; selecting S10(196Hz-215Hz)、S12(235Hz-255Hz)、S15(294Hz-313Hz)、S21(412Hz-431Hz)、S33(647Hz-666Hz)、S40(784Hz-804Hz) is taken as a characteristic value of the rolling bearing diameter inconsistency fault of the rolling bearing;
when the motor bearing generates nonlinear vibration, the characteristic frequency of the axial vibration is integral multiple or fractional multiple of the rotation frequency, so that S of the axial vibration signal is selected0(0Hz-19Hz)、S1(19Hz-39Hz)、S3(59Hz-78Hz)、S7(138Hz-158Hz)、S10(196Hz-215Hz)、S14(275Hz-294Hz) as the characteristic value for judging the bearing nonlinear vibration fault.
And (4) prediction judgment: inputting the characteristic components as input into a radial basis function neural network, and calculating an output value, specifically:
Figure BDA0003242113020000051
wherein the basis functions are:
Figure BDA0003242113020000052
a Radial Basis (RBF) neural network having a three-layer structure: an input layer, an intermediate layer and an output layer, which has only one hidden layer, the hidden unit is a basic function phi (x, x)i)。
The training of the radial basis function neural network comprises outputting a unit weight omegaiTraining ofCenter x of hidden unitiTraining of (2) and training of the function width σ; to the weight omega of the output unitiThe training of (2) is directly calculated by using a least square method, and the center x of the hidden unit is subjected toiThe two parameters of the training and the function width sigma adopt a K-mean clustering method to cluster the samples into M classes, and the class center is used as the center of the RBF, so that the function width is further determined.
In the embodiment, 30 characteristic values of current and vibration are used as input X, the input is 30, the number of hidden layer units is also 30, and the standard reference value of the corresponding input sample is used as the center X of 30 corresponding hidden units through clusteringiWidth σ (σ) of the corresponding hidden cell1，σ2，…，σ25) (ii) a Output unit weight omega (omega)1，ω2，…，ω25) The output f (x) is the predicted output value, i.e. the health status of the device.
The output values are integer values such as 0, 1, 2, 3, 4, 5, 6, etc., and the state of the equipment is judged by presetting, wherein 0 is used for indicating normal in the embodiment; 1 indicates a rotor bar break fault; 2, a stator core and stator coil loosening fault is shown; 3 represents an air gap dynamic eccentricity fault; 4, indicating the fault that the diameters of the rolling bodies of the rolling bearings are inconsistent; 5 represents bearing nonlinear vibration failure; and 6, air gap eccentricity fault. Different fault states can be automatically classified according to newly appeared sample data in later operation.
The output frequency of each fault condition is as follows:
(1) when the motor has a rotor broken bar fault, characteristic components can appear in the frequency spectrum of the stator wire current signal, and the following relations are satisfied:
fb＝(1±2ks)f1 (1)；
in the formula: f. ofbThe characteristic frequency of the motor with the rotor broken bar fault is obtained; f. of1Is the power supply frequency; s is slip; k is a positive integer; the most obvious characteristic is that k is 1, namely, the side frequency occurs near the power frequency.
(2) When the motor has a rotor broken bar fault, characteristic components can appear in a vibration signal frequency spectrum, and the frequency is as follows:
Figure BDA0003242113020000061
in the formula (f)1Is the power supply frequency; s is slip; r ═ 1, ± 2, ± 3 …; p is the number of pole pairs; and judging whether the rotor broken bar fault exists or not by measuring the vibration signal of the motor and carrying out frequency spectrum analysis on the vibration signal.
(3) When the motor has an eccentric fault, a specific frequency is generated in the stator current, and the formula is as follows:
fec＝f1±fr (3)
in the formula (I), the compound is shown in the specification,
Figure BDA0003242113020000062
is the motor speed frequency, and the eccentricity fault also can be generated in the frequency spectrum of the vibration signal at 1/2sf1Periodically pulsating electromagnetic vibrations.
Device assembly
A device for detecting a device failure, as shown in fig. 2, has a general structure including: the system comprises an embedded microcomputer, a data acquisition card, a vibration sensor, a current sensor, a sound sensor, a temperature sensor, a humidity sensor and a communication interface, wherein an analysis platform comprises MATLABLE and C + +. The data acquisition card is inserted into a PCI slot of the embedded microcomputer, 6 channels of the acquisition card are set to acquire signals with the frequency of 10kHz and vibration sensors, 1 channel of the acquisition card acquires signals with the frequency of 2kHz and current sensors, the signals are read by using MATLABLE, and the temperature sensors and the humidity sensors are connected into 485 serial ports expanded out through USB interfaces. The whole hardware device is packaged in a box body, so that a patroller can bear the back to move forward, and the probe can be conveniently accessed into (or on) the equipment to acquire information after the patroller arrives at the equipment.
The method specifically comprises the following steps:
an infrared temperature measuring gun (integrated with infrared temperature measurement, IC card identification and a camera) is connected to a COM6 port and a USB interface of a 485 serial port of an embedded computer, and a COM6 is required to be set in a BIOS of the embedded computer to be in a 485 serial port communication mode;
an environment temperature and humidity sensor is connected to a COM5 port of a 485 serial port of the embedded computer, and a COM5 is required to be set in a BIOS of the embedded computer to be in a 485 serial port communication mode;
the sound collector is connected to the acquisition card of the embedded computer;
the high-speed data acquisition card USB3100 is connected to the USB interface of the embedded computer;
the vibration sensor and the current sensor are connected to the high-speed data acquisition card USB 3100;
and the battery data acquisition card is connected to a COM3 port of a 232 serial port of the embedded computer.
Data acquisition:
temperature data acquisition:
a main function flow, wherein a QSeriol port serial port object is created and initialized to COM6, the baud rate is 9600, the data bit is 8, the verification is none, the stop bit is one bit, and the serial port is opened; connecting to the Mysql database xjx; circularly checking whether the data is received in the serial port cache, waiting for continuously circularly checking the serial port cache if the data is not received, checking whether the data is complete or not and whether CRC (cyclic redundancy check) is correct or not if the data is received, and resolving the temperature and IC (integrated circuit) data and replying confirmation data to an infrared temperature measuring gun if the data is not in a problem; inquiring a data table f _ status, judging whether equipment which is in temperature measurement and inspection exists, if not, waiting for continuous cyclic inspection of serial port cache, and if so, calling an environment temperature and humidity acquisition function to acquire environment temperature and humidity; storing the previously collected infrared temperature, IC information, environmental temperature and humidity data into an f _ wd table of a database; then calling a camera shooting function, creating a QTcpSecket object, establishing TCP connection with camera management software, issuing a shooting command to the camera management software, executing a shooting action by a camera, and storing a picture containing information of the measured equipment and the current infrared temperature measuring point into a patrol instrument; if the program exits, the database connection is closed, and the serial port is closed.
Acquiring a function flow of environment temperature and humidity, establishing a QSeriol port serial port object, initializing the QSeriol port serial port object to COM5, setting the Baud rate to be 9600, setting the data bit to be 8, checking to be none, setting the stop bit to be one bit, and opening the serial port; and issuing an acquisition command to an acquisition module, waiting for receiving data replied by the acquisition module, checking whether the data is complete or not and whether CRC (cyclic redundancy check) is correct or not, analyzing and transmitting the temperature and humidity data, and closing the serial port.
The method comprises the steps of camera shooting function flow, creating a QTcpSect client object, establishing TCP communication with camera management software of a local machine, sending a shooting command to the camera management software, waiting for receiving state data of whether shooting is successful, and closing TCP connection.
Sound data acquisition:
the method comprises the steps of transmitting a file storage path, creating a QAudioRecorder object, setting a QAudioEncoderSettings parameter, coding the parameter into audio/pcm, enabling the sampling rate to be 8kHz, enabling the format of a storage file to be audio/x-wav, calling a recording starting function record, and enabling subsequent playback.
Vibration and current data acquisition:
connecting a Mysql database xjx, creating a USB3100 device object, setting the acquisition frequency of 6 channels of an acquisition card to 10kHz, acquiring signals of a vibration sensor, setting the acquisition frequency of 1 channel to 2kHz, acquiring signals of a current sensor, initializing AI parameters, starting AI sampling, and setting forced triggering; checking the following steps in a circulating manner, checking whether a computer is required to be closed by a table f _ close, checking whether a polling device for acquiring vibration data exists in the table f _ status, if the previous two steps are not continuously circulated, checking and creating a vibration data file storage path and a file, creating a vibration data day table, calling sound data acquisition software and starting timing, circularly checking the state of the vibration acquisition device and reading AI data, then storing the AI data until the acquisition is finished, operating vibration data analysis software, storing an analysis result into a database, waiting for 10 seconds for the sound acquisition timing to close the sound acquisition software, and continuously performing the previous circulating check; and when the program exits, closing the database connection, stopping AI sampling, releasing AI resources and releasing the equipment object.
Collecting battery data:
creating a QSeriolPort serial port object, initializing the QSeriolPort serial port object to COM3, setting the Baud rate to 9600, setting the data bit to 8, checking to be none, setting the stop bit to one bit, and opening the serial port; connecting to the Mysql database xjx; circularly checking whether the data are received in the serial port receiving cache region, waiting for continuing to circularly check the serial port receiving cache region if the data are not received, checking whether the data are complete if the data are received, and analyzing the data such as the battery power, the working current, the working temperature, the percentage of the residual power, the residual power and the like if the data have no problems; saving the parsed data to a battery data calendar of the database xjx _ dy; if the program exits, the database connection is closed, and the serial port is closed.
Before the equipment mirror image detection, the inspection box is self-checked, after an interface is requested to acquire data, temperature measurement detection is started, and the temperature is detected to be normal by pressing a handheld switch; carrying out vibration detection, wherein the collected data is normal in the range of the sensing device; and (4) taking the battery data, and completing self-checking within a normal power supply range. And detecting the equipment in a normal operation state. Firstly, scanning a two-dimensional code of equipment, entering a routing inspection page of the current equipment, acquiring temperature measurement and vibration points, detecting the equipment, collecting temperature, acquiring the temperature, the environment temperature and humidity and an nth point photo of the nth point, completing the temperature collection of the nth point, and repeating the steps for collection if the measured temperature points do not reach the total number; acquiring vibration, namely acquiring a sound recording file, a vibration data file and an nth data table from an nth point; if the number of the measured vibration points does not reach the total number, repeating the steps for collection; and after all the measuring points are measured, detecting the next device.
The intelligent system realizes the intellectualization of the daily routing inspection of the equipment by comprehensively sensing the equipment and utilizing advanced signal processing and artificial intelligence technology, improves the accuracy of analysis and diagnosis and ensures the safe operation of the equipment. The device disclosed by the invention adopts the embedded microprocessor and uses the battery as a power supply, so that the device is further miniaturized and lightened, the labor intensity of inspection personnel is reduced, and the inspection efficiency of equipment is improved.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A method for detecting equipment faults is characterized by comprising the following specific steps:
collecting data: collecting various signal data of each collection point of the equipment;
and (3) data analysis: analyzing the frequency spectrum of the signal data according to the fault type to obtain a characteristic component;
and (3) calculating and outputting: calculating an output value of the neural network by taking the characteristic component as an input of the neural network;
and (3) fault judgment: and judging the state of the equipment according to the output value.
2. The method of claim 1, wherein the detection device is self-tested before data is collected.
3. The method of claim 1, wherein the fault types include a rotor bar breakage fault, a stator core and stator coil loosening fault, an air gap dynamic eccentricity fault, a rolling bearing rolling element diameter inconsistency fault, a bearing nonlinear vibration fault, and an air gap eccentricity fault.
4. The method for detecting the equipment fault according to claim 3, wherein the frequency of the characteristic component of the rotor broken bar fault in the frequency spectrum of the stator wire current signal is as follows:
fb＝(1±2ks)f1 (1)；
in the formula: f. ofbCharacteristic frequency of rotor broken bar fault in frequency spectrum of stator line current signal; f. of1Is the power supply frequency; s is slip; k is a positive integer.
5. The method for detecting the equipment fault according to claim 3, wherein the frequency of the characteristic component of the rotor broken bar fault in the vibration signal frequency spectrum is as follows:
Figure FDA0003242113010000011
in the formula: f. ofbzCharacteristic frequency of rotor broken bar fault in vibration signal frequency spectrum; f. of1Is the power supply frequency; s is slip; r ═ 1, ± 2, ± 3 … ± n; p is the number of pole pairs.
6. The method of claim 3, wherein the frequency of the characteristic component of the eccentric fault in the frequency spectrum of the stator current signal is:
fec＝f1±fr (3)；
in the formula (I), the compound is shown in the specification,
Figure FDA0003242113010000021
is the motor speed frequency; f. of1Is the power supply frequency; s is slip; p is the number of pole pairs.
7. A method of detecting a fault in an apparatus according to claim 3, wherein the eccentricity fault is characterized in the vibration signal spectrum by: generated at 1/2sf1Periodically pulsating electromagnetic vibrations.
8. The method for detecting equipment faults as claimed in claim 3, wherein the characteristic frequency of the vibration signal in the bearing nonlinear vibration fault is an integral multiple or a fraction multiple of the rotation frequency.
9. An apparatus for detecting a device fault, comprising: the temperature measuring device comprises an embedded microcomputer, a vibration and current collecting device, a sound collecting device, a temperature measuring device and an environment temperature and humidity collecting device, wherein the sound collecting device, the temperature measuring device and the environment temperature and humidity collecting device are all connected with the embedded microcomputer; the vibration and current acquisition device is connected with the embedded microcomputer through an acquisition card.
10. The device for detecting the equipment failure according to claim 9, further comprising a battery data acquisition device, wherein the battery data acquisition device is connected with the embedded microcomputer.
CN202111022718.7A 2021-09-01 2021-09-01 Device and method for detecting equipment fault Pending CN113703371A (en)

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