CN112034339A - Servo motor fault diagnosis method based on LVQ neural network - Google Patents

Servo motor fault diagnosis method based on LVQ neural network Download PDF

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CN112034339A
CN112034339A CN201910467985.1A CN201910467985A CN112034339A CN 112034339 A CN112034339 A CN 112034339A CN 201910467985 A CN201910467985 A CN 201910467985A CN 112034339 A CN112034339 A CN 112034339A
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邱德敏
梁波
范海涛
焦鹏
任东辉
张琪
祝敬乐
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UNIT 63756 OF PLA
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Abstract

The invention discloses a servo motor fault diagnosis method based on an LVQ neural network, which comprises the steps of collecting steady-state current of a servo motor during the period that a measurement and control antenna tracks a satellite, carrying out filtering and noise reduction processing on historical data of the steady-state current, extracting characteristic parameter samples, designing, training and detecting the neural network, and diagnosing motor faults through the neural network. The LVQ neural network can be trained through the collected marked data, automatic acquisition of diagnosis knowledge is achieved, and fault automatic diagnosis is conducted through the LVQ neural network obtained through training.

Description

Servo motor fault diagnosis method based on LVQ neural network
Technical Field
The invention relates to a servo motor fault diagnosis method based on an LVQ neural network, which is used for measuring the current of a motor of a measurement and control antenna and giving a diagnosis result when the motor has a fault.
Background
In the fault diagnosis process, fault mode identification is a key link for realizing fault on-line monitoring and intelligent diagnosis and is also the core of fault diagnosis, and the identification efficiency and accuracy directly influence the quality of a fault diagnosis result. The servo motor has a complex structure, the fault characteristic parameters have the characteristics of dispersity, randomness and fuzziness, and an accurate mathematical model cannot be established because a clear linear relation does not exist between a fault mode and the fault characteristic parameters. In this case, how to obtain complete and effective diagnostic knowledge becomes a "bottleneck" problem in building a servo motor failure mode recognition system model. Aiming at the problem, the LVQ (Learning Vector Quantization) neural network is used for solving the problem of fault mode identification, the LVQ neural network is trained through the collected marked data, the automatic acquisition of diagnosis knowledge is realized, and the fault automatic diagnosis is carried out through the LVQ neural network obtained through training.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides a servo motor fault diagnosis method based on an LVQ neural network.
The technical scheme of the invention is as follows: a servo motor fault diagnosis method based on an LVQ neural network comprises the following steps:
a servo motor fault diagnosis method based on an LVQ neural network is characterized by comprising the following steps:
A) collecting a steady-state current signal when the motor runs, wherein the steady-state current signal comprises normal running current data and fault current data of the motor to form motor steady-state current historical data;
B) filtering and denoising the motor steady-state current historical data recorded in the step A), and calculating current pulse frequency, a steady-state current mean value, a steady-state current standard deviation, a starting current peak value when the motor is just started and a current change rate of a peak value point as characteristic parameters;
C) using the characteristic parameters of the step B) as samples, wherein the format of the samples is as follows: each piece of data is organized according to an input-output mode, the input is a motor current characteristic parameter, the output is a motor fault mode, and a sample is divided into a training sample and a detection sample;
D) designing a structure of a neural network according to the sample of the step C), and setting the number of neurons of a competition layer, the connection weight between a characteristic parameter layer and the competition layer and the connection relation between the competition layer and a fault mode layer;
E) carrying out neural network training, setting the maximum training algebra and learning rate of the LVQ neural network, and training the neural network model determined in the step D) by using the training samples collected in the step C);
F) performing neural network simulation, storing the network after the network reaches the preset maximum training times, inputting 5 quantitative characteristics in the test set into the network, and outputting the diagnosis result;
G) and F) analyzing the diagnosis result to obtain the misdiagnosis rate, wherein the misdiagnosis rate comprises the steps of diagnosing the fault-free state as the misdiagnosis between the fault state and different fault states, returning to the step E) when the misdiagnosis rate is higher than a preset receiving range, and continuing to train the neural network until the misdiagnosis rate meets the requirement.
Further, the step B) adopts wavelet denoising; and D) adopting an LVQ neural network, wherein the neural network comprises an input value, an output value, the number of layers, the number of nodes of each layer and a judgment threshold value of each layer.
The invention has the beneficial effects that:
(1) the servo motor fault diagnosis method based on the LVQ neural network can train a competition layer in a teacher state, and input vectors are combined and classified according to the category of a target through a hidden layer transfer function of the competition layer, so that the problems that the working state of a servo motor is complex, and no obvious linear relation exists between a fault mode and fault characteristic parameters are solved.
(2) Aiming at the unbalanced problem that few fault samples and many normal samples exist in fault diagnosis, the invention provides the connection weight factor on the competition layer of the LVQ neural network, thereby improving the classification precision of small samples and reducing the misjudgment loss.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of a neural network of the present invention.
Detailed Description
Embodiments of the present invention are further described below with reference to the drawings, and the embodiments of the present invention include, but are not limited to, the following embodiments.
As shown in fig. 1, a servo motor fault diagnosis method based on an LVQ neural network includes the following steps:
A) and collecting steady-state current signals when the motor runs, wherein the steady-state current signals comprise normal running current data and fault current data of the motor, and forming motor steady-state current historical data.
B) And B), filtering and denoising the motor steady-state current historical data recorded in the step A), and calculating current pulse frequency, a steady-state current mean value, a steady-state current standard deviation, a starting current peak value when the motor is just started, and a peak point current change rate as characteristic parameters.
The characteristic parameter calculation method comprises the following steps, taking a permanent magnet direct current servo motor as an example, and the dynamic mathematical model of the motor during no load is as follows:
Figure BDA0002082263690000031
Figure BDA0002082263690000032
in the formulas (1) and (2), u is the armature voltage, i is the armature current, and R isaIs armature resistance, LaIs armature inductance, C is motor constant, J is rotor moment of inertia, and Ω is motor angular velocity，TfIs the dry friction torque coefficient of the motor, CfThe coefficient of viscous friction of the motor.
The no-load starting armature current of the permanent magnet direct current motor can be approximately obtained by the formula (1) and the formula (2):
Figure BDA0002082263690000033
in the formula (3), TMIs the electromechanical time constant, T, of the motorM＝JRa/C2。
As can be seen from equation (3), the current signal is approximated to an exponential curve in the starting process of the permanent magnet dc motor, and the starting current peak value is:
Figure BDA0002082263690000041
from the equation (3), the starting current decreases rapidly and the electromechanical time constant TMRelated, TMThe smaller the starting current decreases the faster. When t is 0, the change rate near the current peak point in the starting process of the motor is as follows:
Figure BDA0002082263690000042
from the formula (4) to see ImThe motor armature resistance is inversely proportional to the motor armature resistance, and can be used as one of characteristic parameters for diagnosing armature resistance faults.
The permanent magnet direct current motor can meet the voltage balance and torque balance equation during steady state operation:
u＝Rai+CΩ (6)
Ci＝Tf+CfΩ (7)
the steady-state current of the permanent magnet direct current motor obtained by the formulas (6) and (7) is as follows:
Figure BDA0002082263690000043
in an electric machineIn the operation process, the recorded steady-state current data of a certain section of motor can be averaged to obtain a steady-state current mean value iavFurther, the variance of the data can be obtained to obtain the standard deviation i of the steady-state currentstd。
For a direct current motor, when a brush is subjected to primary commutation, an armature current pulsates once, the number of times of the armature current pulsation is fixed in the process of one rotation of a motor rotor, a high-frequency component, called the pulsation frequency, is superposed on a steady-state current of a permanent magnet direct current motor, and the relationship is satisfied:
Figure BDA0002082263690000051
in the formula (9), p is the number of motor phase-changing sheets, and n is the motor rotating speed (r/min).
Above current ripple frequency fwSteady state current i, starting current peak imAnd the value obtained by the calculation method of the peak point current change rate k is a theoretical value and can be used as a reference of an actual measurement value.
C) Using the characteristic parameters of the step B) as samples, wherein the format of the samples is as follows: each piece of data is organized according to an input-output mode, the input is a motor current characteristic parameter, the output is a motor fault mode, and the sample is divided into a training sample and a detection sample.
D) Designing the structure of the neural network according to the samples in the step C), and setting the number of the neurons of the competition layer, the connection weight between the characteristic parameter layer and the competition layer and the connection relation between the competition layer and the fault mode layer.
As shown in fig. 2, fault diagnosis is performed by using an LVQ neural network, which is a learning algorithm for training a competition layer in a teacher-present state, and can combine and classify input vectors according to the class of a target by using a hidden layer transfer function of the competition layer, so that the LVQ algorithm can be considered as a calculation for improving a self-organizing feature mapping algorithm into teacher-present learning.
The basic idea is as follows: calculating the competition layer neuron closest to the input vector so as to find a linear output layer neuron connected with the competition layer neuron, wherein if the category of the input vector is consistent with the category corresponding to the linear output layer neuron, the weight value of the corresponding competition layer neuron moves along the direction of the input vector; otherwise, the corresponding competition layer neuron weight value moves along the reverse direction of the input vector.
1) Model input
In the actual operation of the servo direct current motor, there are many characteristic quantities which can represent the dynamic behavior of the system, but these characteristic quantities are not necessarily all selected as input characteristic parameters. In order to meet the requirements of online monitoring and intelligent diagnosis of a servo direct current motor fault diagnosis system on real-time performance, simple calculation, easy processing and the like and the characteristics of strong sensitivity of characteristic quantity to working state change, good regularity and the like, the current pulse frequency f is selected according to the analysis result of related documentswSteady state current average value iavSteady state current standard deviation istdPeak value of starting current imAnd the peak point current change rate k is used as a characteristic quantity for judging the fault of the servo direct current motor, so that the characteristic parameter for fault diagnosis is Te＝[fw,iav,istd,im,k]. The input vector of the corresponding LVQ neural network for diagnosing the fault of the servo direct current motor is X ═ X1,x2,x3,x4,x5]T。
Wherein x1 represents the current ripple frequency fwAnd x2 represents the steady-state current average value iavAnd x3 represents the steady-state current standard deviation istdAnd x4 represents the starting current peak imAnd x5 represents the peak point current change rate k.
2) Model output
The fault types of the servo direct current motor mainly include three types of electric brush faults, element open circuits and turn-to-turn short circuits. Therefore, the failure modes of the corresponding servo motor have four states of no failure, brush failure, element open circuit and turn-to-turn short circuit. When the four states are marked as 1,2, 3 and 4 respectively, the corresponding output vector is C ═ C1,c2,c3,c4]TFault status is coded as no fault (1,0,0,0), brush fault (0,1,0,0), open element (0,0,1,0),turn-to-turn short (0,0,0, 1).
The problem translates into an optimal partitioning of the five-dimensional euclidean space into a 4-dimensional decision space.
3) Model network structure
The structure of the device is shown in fig. 2 and comprises a characteristic parameter layer, a competition layer and a failure mode layer. The number of the neurons of the characteristic parameter layer is 5, the neurons correspond to five input variables, the number of the neurons of the fault mode layer is 4, and the neurons correspond to four output variables. The number of the neurons of the competition layer is 8, and every two neurons correspond to a fault state. The neural network is completely connected between the characteristic parameter layer and the competition layer, wherein WiAnd the connection weight vectors represent the ith competition neuron and five characteristic parameters, and are partially connected in a competition layer and a failure mode layer, and each output neuron is connected with a different group of competition neurons. The connection weight of the competition layer and the failure mode layer is fixed to be 1. In the LVQ network training process, the connection weight of the characteristic parameter layer and the competition layer is gradually adjusted to be the clustering center. When a sample is sent to the network, the competing neuron whose reference vector is closest to the input pattern wins the competition due to the acquisition of the excitation, the output is 1, and the other neurons output 0. The output neurons that produce 1 give a class of input patterns, each output neuron representing a different class.
4) The solving steps of the LVQ neural network model for the fault diagnosis of the servo motor are as follows:
initializing connection weight W between a characteristic parameter layer and a competition layerj(j ═ 1,2, …, m, which represents the weight of the j-th competition layer neuron and the feature parameter vector), and the initial learning rate η (0) (η > 0) and the training times t are determinedm；
② changing the characteristic parameter vector X into [ X ═ X1,x2,x3,x4,x5]TAnd (3) sending the characteristic parameter layer, and calculating the distance between neurons of the competition layer and the characteristic parameter vector according to the formula (10):
dj＝||X-Wj|| (10)
selecting the neuron of the competition layer with the minimum distance with the characteristic parameter vector if diAt minimum, thenNoting the failure mode label of the failure mode layer neuron connected with it as ciThe actual corresponding failure mode label is cx；
Comparing the actual output of the failure mode with the target output to adjust the weight of the winning neuron, and assuming that the neuron of the winning competitive layer is the ith neuron:
if the classification is correct, then ci＝cxAdjusting the weight value to the input sample direction
Wi (t+1)＝Wi t+η[X-Wi t] (11)
If the classification is not correct, i.e. ci≠cxAdjusting the weight in the opposite direction to the input sample
Wi (t+1)＝Wi t-η[X-Wi t] (12)
Update learning rate
η(k)＝η(0)(1-t/tm) (13)
When t < tmAnd (4) turning to the step (c), inputting a next marked fault mode training sample, and repeating the steps until t is t +1m。
E) And C), carrying out neural network training, setting the maximum training algebra and learning rate of the LVQ neural network, and training the neural network model determined in the step D) by using the training samples collected in the step C).
Acquiring operating characteristic parameter data and fault mode data of the servo motor, wherein 500 groups of sample data and 20 groups of test data are acquired, and part of data are shown in tables 1 and 2:
table 1 partial sample data
Figure BDA0002082263690000081
Table 2 partial test data
Figure BDA0002082263690000082
The network learning rate is set to 0.1, the training times are set to 200, the initial weight setting between the feature vector layer and the competition layer is shown in table 3, and the connection setting between the competition layer and the failure mode layer is shown in table 4.
Table 3 initial weight setting table between feature vector layer and competition layer
Figure BDA0002082263690000083
Figure BDA0002082263690000091
Table 4 connection setup table between contention layer and failure mode layer
Figure BDA0002082263690000092
F) And (4) carrying out neural network simulation, storing the network after the network reaches the preset maximum training times, inputting 5 quantitative characteristics in the test set into the network, and outputting the diagnosis result.
The LVQ neural network is subjected to simulation training, after 200 generations of training, the trained LVQ neural network is obtained, the trained neural network is tested using a test data set, and part of the diagnosis results are shown in table 5.
TABLE 5 partial diagnostic results of test data set
Figure BDA0002082263690000093
G) And F) analyzing the diagnosis result to obtain the misdiagnosis rate, wherein the misdiagnosis rate comprises the steps of diagnosing the fault-free state as the misdiagnosis between the fault state and different fault states, returning to the step E) when the misdiagnosis rate is higher than a preset receiving range, and continuing to train the neural network until the misdiagnosis rate meets the requirement.
One group of the 20 groups of test data diagnosis results has diagnosis errors, the misdiagnosis rate is 5 percent, and the results are ideal. The analysis shows that the ideal training result is mainly caused by the large difference of the characteristic parameter vectors corresponding to the fault modes in the simulation training data. The relationship between the change in the fault signature and the fault pattern is shown in table 6.
TABLE 6 relationship between failure signature change and failure mode
Figure BDA0002082263690000101

Claims (3)

1. A servo motor fault diagnosis method based on an LVQ neural network is characterized by comprising the following steps:
A) collecting a steady-state current signal when the motor runs, wherein the steady-state current signal comprises normal running current data and fault current data of the motor to form motor steady-state current historical data;
B) filtering and denoising the motor steady-state current historical data recorded in the step A), and calculating current pulse frequency, a steady-state current mean value, a steady-state current standard deviation, a starting current peak value when the motor is just started and a current change rate of a peak value point as characteristic parameters;
C) using the characteristic parameters of the step B) as samples, wherein the format of the samples is as follows: each piece of data is organized according to an input-output mode, the input is a motor current characteristic parameter, the output is a motor fault mode, and a sample is divided into a training sample and a detection sample;
D) designing a structure of a neural network according to the sample of the step C), and setting the number of neurons of a competition layer, the connection weight between a characteristic parameter layer and the competition layer and the connection relation between the competition layer and a fault mode layer;
E) carrying out neural network training, setting the maximum training algebra and learning rate of the LVQ neural network, and training the neural network model determined in the step D) by using the training samples collected in the step C);
F) performing neural network simulation, storing the network after the network reaches the preset maximum training times, inputting 5 quantitative characteristics in the test set into the network, and outputting the diagnosis result;
G) and F) analyzing the diagnosis result to obtain the misdiagnosis rate, wherein the misdiagnosis rate comprises the steps of diagnosing the fault-free state as the misdiagnosis between the fault state and different fault states, returning to the step E) when the misdiagnosis rate is higher than a preset receiving range, and continuing to train the neural network until the misdiagnosis rate meets the requirement.
2. The LVQ neural network-based servo motor fault diagnosis method according to claim 1, wherein the step B) employs wavelet de-noising.
3. The LVQ neural network-based servo motor fault diagnosis method according to claim 1, wherein step D) adopts a neural network, and the neural network comprises an input value, an output value, the number of layers, the number of nodes of each layer and a judgment threshold value of each layer.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112945892A (en) * 2021-01-25 2021-06-11 西安交通大学 Tumor diagnosis method based on infrared spectrum fusion and learning vectorization neural network
CN115235612A (en) * 2022-08-09 2022-10-25 爱尔达电气有限公司 Intelligent fault diagnosis system and method for servo motor

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5574387A (en) * 1994-06-30 1996-11-12 Siemens Corporate Research, Inc. Radial basis function neural network autoassociator and method for induction motor monitoring
CN103761569A (en) * 2013-12-20 2014-04-30 上海电机学院 Fault diagnosis method and device for wind driven generator
CN106022352A (en) * 2016-05-05 2016-10-12 哈尔滨理工大学 Submersible piston pump fault diagnosis method based on support vector machine
CN106778594A (en) * 2016-12-12 2017-05-31 燕山大学 Mental imagery EEG signal identification method based on LMD entropys feature and LVQ neutral nets
CN107766816A (en) * 2017-10-18 2018-03-06 河海大学 A kind of Mechanical Failure of HV Circuit Breaker recognition methods based on LVQ neutral nets
CN108152651A (en) * 2017-12-27 2018-06-12 重庆水利电力职业技术学院 Transmission line malfunction integrated recognition method based on GMAPM and SOM-LVQ-ANN
CN109325553A (en) * 2018-12-04 2019-02-12 山东建筑大学 A kind of wind turbine gearbox fault detection method, system, equipment and medium

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5574387A (en) * 1994-06-30 1996-11-12 Siemens Corporate Research, Inc. Radial basis function neural network autoassociator and method for induction motor monitoring
CN103761569A (en) * 2013-12-20 2014-04-30 上海电机学院 Fault diagnosis method and device for wind driven generator
CN106022352A (en) * 2016-05-05 2016-10-12 哈尔滨理工大学 Submersible piston pump fault diagnosis method based on support vector machine
CN106778594A (en) * 2016-12-12 2017-05-31 燕山大学 Mental imagery EEG signal identification method based on LMD entropys feature and LVQ neutral nets
CN107766816A (en) * 2017-10-18 2018-03-06 河海大学 A kind of Mechanical Failure of HV Circuit Breaker recognition methods based on LVQ neutral nets
CN108152651A (en) * 2017-12-27 2018-06-12 重庆水利电力职业技术学院 Transmission line malfunction integrated recognition method based on GMAPM and SOM-LVQ-ANN
CN109325553A (en) * 2018-12-04 2019-02-12 山东建筑大学 A kind of wind turbine gearbox fault detection method, system, equipment and medium

Non-Patent Citations (11)

* Cited by examiner, † Cited by third party
Title
刘曼兰等: "永磁直流电动机故障机理分析与研究", 《微特电机》 *
刘曼兰等: "永磁直流电动机故障检测与智能诊断装置", 《微特电机》 *
刘震等: "基于学习矢量量化网络的智能BIT故障诊断方法", 《测控技术》 *
孙健等: "基于PCA-LVQ的模拟电路故障诊断", 《电路与***学报》 *
嵇斗等: "基于D-S证据理论的直流电机故障诊断研究", 《电机与控制应用》 *
廖勇等: "基于虚拟阻抗的永磁同步电机电流环控制策略", 《中国电机工程学报》 *
成振华等: "基于BP神经网络的大功率直流电机故障诊断研究", 《电气自动化》 *
梁武科等: "基于LVQ网络的水电机组振动多故障诊断方法", 《农业机械学报》 *
梁武科等: "水电机组振动故障诊断的人工神经网络选择研究", 《仪器仪表学报》 *
王凯等: "LVQ神经网络在煤矿风机故障诊断中的研究", 《煤矿机械》 *
田瑶瑶: "基于机器学习的机电***关键部件PHM技术研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112945892A (en) * 2021-01-25 2021-06-11 西安交通大学 Tumor diagnosis method based on infrared spectrum fusion and learning vectorization neural network
CN115235612A (en) * 2022-08-09 2022-10-25 爱尔达电气有限公司 Intelligent fault diagnosis system and method for servo motor

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