CN109447187B - Motor fault diagnosis method and system - Google Patents

Motor fault diagnosis method and system Download PDF

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CN109447187B
CN109447187B CN201811590223.2A CN201811590223A CN109447187B CN 109447187 B CN109447187 B CN 109447187B CN 201811590223 A CN201811590223 A CN 201811590223A CN 109447187 B CN109447187 B CN 109447187B
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阳春华
魏焱烽
陈志文
彭涛
杨超
陶宏伟
桂卫华
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Central South University
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Abstract

本发明涉及电机故障诊断领域，公开了一种电机故障诊断方法及系统，以及时发现电机运行的异常情况并进行故障诊断，使用方便，易于实施；本发明的方法包括：选取正常训练数据集，计算第一检测统计量，并计算检测阈值；选取不同类型的故障数据作为故障训练数据集，计算第二检测统计量，采用核密度估计法计算第二检测统计量的故障概率密度函数，并构建所有类型故障样本的概率密度函数集；选取测试数据集，根据测试数据集计算第三检测统计量，将第三检测统计量与检测阈值进行比较，判断电机是否发生故障；若发生故障，采用核密度估计法计算第三检测统计量的故障密度函数，并构建测试数据集的概率密度函数集，从而诊断故障类型。

Figure 201811590223

The invention relates to the field of motor fault diagnosis, and discloses a motor fault diagnosis method and system, which can timely discover abnormal conditions of motor operation and carry out fault diagnosis, which is convenient to use and easy to implement; the method of the invention comprises: selecting a normal training data set, Calculate the first detection statistic, and calculate the detection threshold; select different types of fault data as the fault training data set, calculate the second detection statistic, use the kernel density estimation method to calculate the fault probability density function of the second detection statistic, and construct Probability density function set of all types of fault samples; select the test data set, calculate the third detection statistic according to the test data set, and compare the third detection statistic with the detection threshold to determine whether the motor fails; The density estimation method calculates the fault density function of the third detection statistic, and constructs the probability density function set of the test data set, thereby diagnosing the fault type.

Figure 201811590223

Description

Motor fault diagnosis method and system
Technical Field
The invention relates to the field of motor fault diagnosis, in particular to a motor fault diagnosis method and system.
Background
The motor is an electric device widely used in various industries, such as a high-speed train traction transmission system, a wind power generator, a new energy automobile driving motor, a track traction motor, a ship motor and the like. In practical application, the motor is affected by frequent starting, load fluctuation, harsh working environment and other factors, so that the motor running state is abnormal and further a fault is inevitably generated. If the faults cannot be diagnosed and discovered in time, the faults can be continuously worsened, finally the whole system is out of work, and huge losses are brought to industrial production.
In actual operation of the motor, such as switching between states of starting acceleration, constant speed, braking deceleration and the like, dynamic characteristics shown under different working conditions are different, and changes of system parameters and measurement data are complex. For example, when the motor operates under the working condition of starting acceleration, the output torque is large, the corresponding starting current is large, and the voltage of the motor is increased; under the working condition of constant speed, if the motor runs at low speed, the current and the magnetomotive force of a motor winding are large; if the motor runs at a speed higher than the rated speed, the motor also needs to be subjected to weak magnetic control, and the magnetomotive force of the motor obviously changes; under the braking condition, taking electric braking as an example, when the motor works in a generator mode, the output torque is reversed, mechanical energy is converted into electric energy to be fed back to a power grid again or consumed through a braking resistor, and the current, magnetomotive force, rotating speed and the like of a stator of the motor are greatly changed. Therefore, the dynamic change of the motor operation is complex and has a plurality of parameters, and the factors causing the motor to break down are numerous.
Therefore, how to find the abnormal condition of the motor operation in time and carry out fault diagnosis becomes a problem which needs to be solved urgently.
Disclosure of Invention
The invention aims to provide a motor fault diagnosis method and system, which can find abnormal conditions of motor operation in time and carry out fault diagnosis, and are convenient to use and easy to implement.
In order to achieve the above object, the present invention provides a motor fault diagnosis method, comprising the steps of:
s1: selecting normal operation data of a motor to be detected as a normal training data set, calculating a first detection statistic according to the normal training data set, and calculating a detection threshold according to the first detection statistic;
s2: selecting different types of fault data from historical fault operation data as a fault training data set, calculating second detection statistics according to the fault training data set, calculating a fault probability density function of the second detection statistics by adopting a kernel density estimation method, and constructing probability density function sets of all types of fault samples;
s3: selecting real-time operation data of a motor to be tested as a test data set, calculating third detection statistic according to the test data set, comparing the third detection statistic with the detection threshold, if the value of the third detection statistic is larger than the detection threshold, judging that a fault occurs, and entering S4; otherwise, the test data set is selected again until the motor is detected to be in fault;
s4: and calculating a probability density function of the third detection statistic by adopting a kernel density estimation method, constructing a probability density function set of a test data set, and diagnosing the fault type according to the distance between the probability density function set of the test data set and the probability density function sets of all types of fault samples in the S2.
Preferably, the S1 specifically includes the following steps:
s11: the normal training data set X is represented as:
Figure BDA0001920072880000021
in the formula, m is the number of the motor sensors, N is the number of sampling points, x is a motor operation data sample collected according to a time sequence,
Figure BDA0001920072880000022
is a real number set;
s12: carrying out normalization pretreatment on the data set X to obtain a data set
Figure BDA0001920072880000023
Comprises the following steps:
Figure BDA0001920072880000024
computing a data set
Figure BDA0001920072880000025
The calculation formula of the mean matrix μ is:
Figure BDA0001920072880000026
in the formula (I), the compound is shown in the specification,
Figure BDA0001920072880000027
computing a data set
Figure BDA0001920072880000028
The calculation formula of the covariance matrix S is as follows:
Figure BDA0001920072880000029
calculating a first detection statistic by adopting a sliding window method, wherein the calculation formula is as follows:
Figure BDA0001920072880000031
wherein l (k) is the kth normal training sample set
Figure BDA0001920072880000032
Is detected in the first detection statistic of (a),
Figure BDA0001920072880000033
for the kth normal training sample set
Figure BDA0001920072880000034
The average value matrix of (a) is,
Figure BDA0001920072880000035
for the kth normal training sample set
Figure BDA0001920072880000036
Of the covariance matrix, S-1Is the inverse matrix of the covariance matrix of the normal training data set, | | | is a two-norm, tr () is the trace of the matrix;
after h sliding windows, h first detection statistics of the normal training data set are obtained as follows: l (1), …, l (h);
s13: randomly extracting B statistics from h first detection statistics of a normal training data set to form a set { l }1,…,lBB < h, and reordering the B sample samples in descending order: l(1)＜l(2)＜…＜l(w)And (3) making the w-th maximum statistic value w be lambda multiplied by alpha, wherein alpha is an allowable false alarm rate, and lambda is a random sampling frequency, and obtaining the following result after repeating lambda sampling:
Figure BDA0001920072880000037
calculating a detection threshold JthThe calculation formula is as follows:
Figure BDA0001920072880000038
preferably, the S2 specifically includes the following steps:
s21: selecting n types of fault data samples from historical fault operation data, wherein the f type of fault samples form a fault training data set XfComprises the following steps:
Figure BDA0001920072880000039
wherein f is 1, …, n;
for data set XfCarrying out normalization processing to obtain a fault training data set
Figure BDA00019200728800000310
Comprises the following steps:
Figure BDA00019200728800000311
wherein f is 1, …, n;
s22: computing a data set
Figure BDA00019200728800000312
Is a mean matrix of
Figure BDA00019200728800000313
The calculation formula is as follows:
Figure BDA0001920072880000041
in the formula (I), the compound is shown in the specification,
Figure BDA0001920072880000042
wherein d is a slave data set
Figure BDA0001920072880000043
The number of samples extracted in (1), q is the qth fault training data set, and i is 1, …, m, q is 1, …, t;
computing a data set
Figure BDA0001920072880000044
Covariance matrix of
Figure BDA0001920072880000045
The calculation formula is as follows:
Figure BDA0001920072880000046
q fault training sample set for calculating f fault by adopting sliding window method
Figure BDA0001920072880000047
Second detection statistic lf(q) the calculation formula is:
Figure BDA0001920072880000048
after t sliding windows, t second detection statistics of the f-th type fault training sample set are obtained as follows: lf(1),…,lf(t)；
S23: calculating a qth fault training sample set of the f-type fault by adopting a kernel density estimation method
Figure BDA0001920072880000049
Second detection statistic lf(q) fault probability density function Pf(q) the calculation formula is:
Figure BDA00019200728800000410
the probability density function of the t second detection statistics for the class f fault is Pf(1)，…，Pf(t)；
From t probability density functions P by sliding window methodf(1),…,PfG data are sequentially extracted in (t), and an r probability density function set corresponding to the f type fault is formed as follows:
Figure BDA00019200728800000411
wherein r is 1, …, rl。
Preferably, the step of calculating the second detection statistic according to the fault training data set in S3 specifically includes the following steps:
s31: selecting real-time operation data of the motor to be tested as a test data set Y:
Figure BDA0001920072880000051
carrying out normalization pretreatment on the test data set Y to obtain a data set
Figure BDA0001920072880000052
Comprises the following steps:
Figure BDA0001920072880000053
calculating a mean matrix of the test data set Y
Figure BDA0001920072880000054
The calculation formula is as follows:
Figure BDA0001920072880000055
in the formula (I), the compound is shown in the specification,
Figure BDA0001920072880000056
and i is 1, …, m, q is 1, …, t, where d is the number of samples extracted from the data set Y and q is the qth test training data set;
calculating a covariance matrix for test dataset Y
Figure BDA0001920072880000057
The calculation formula is as follows:
Figure BDA0001920072880000058
calculating the qth test sample set Y by adopting a sliding window methodqdThird detection statistic ly(q) the calculation formula is:
Figure BDA0001920072880000059
after t sliding windows, t third detection statistics of the test sample set are obtained as follows: ly(1),…,ly(t)。
Preferably, the normal data includes: the operating voltage, current, power, and speed of the motor.
Preferably, the S4 specifically includes the following steps:
S41：calculating the qth test sample set Y by using a nuclear density estimation methodqdThird detection statistic lyProbability density function P of (q)y(q) the calculation formula is:
Figure BDA0001920072880000061
wherein q is 1, …, t;
t third detection statistics l corresponding to the set of test samplesy(q) a probability density function of Py(1)，…，Py(t)；
From t probability density functions P by sliding window methody(1)，…，PyG data are sequentially extracted in (t), and the probability density function set of the r test sample is formed as follows:
Figure BDA0001920072880000062
wherein r is 1, …, rl；
Calculating the distance between the probability density function set of the r test sample and the probability density function set of the f type fault sample, wherein the calculation formula is as follows:
Figure BDA0001920072880000063
the distance between the probability density function set of the r test sample and the probability density function set of the n types of fault samples is as follows:
Figure BDA0001920072880000064
r under f-type faultlThe distance values are:
Figure BDA0001920072880000065
form it into a distance set
Figure BDA0001920072880000066
Then r islProbability density function set and n classes of individual test samplesThe set of distances between the fault sample probability density function sets are respectively as follows:
Figure BDA0001920072880000067
set D of the above distancesfThe distance data in (1) is rearranged from small to large
Figure BDA0001920072880000068
Calculating the lower quartile of each distance data by adopting a box type graph area calculation method
Figure BDA0001920072880000069
Median number
Figure BDA00019200728800000610
Upper quartile
Figure BDA00019200728800000611
Upper limit of UmaxAnd lower limit UminComprises the following steps:
Figure BDA0001920072880000071
in the formula (I), the compound is shown in the specification,
Figure BDA0001920072880000072
it is indicated that the direction is positive in the upward direction,
Figure BDA0001920072880000073
to get
Figure BDA0001920072880000074
Centralizing data at corresponding positions;
all D are plotted against the calculated values of equation (20)f1, …, n; respectively calculate all corresponding DfArea S of box plotf：
Sf＝IQRf×W；(23)
In the formula, W is the width of the bottom edge;
s42: judging all DfAnd judging the fault type corresponding to the box type graph as the fault type of the motor to be tested.
Preferably, the first detection statistic, the second detection statistic, and the third detection statistic are all of KL detection statistics.
Preferably, in S41, the distance between the r-th test sample probability density function set and the n-type fault sample probability density function set is KL divergence between the r-th test sample probability density function set and the n-type fault sample probability density function set.
As a general inventive concept, the present invention also provides a motor fault diagnosis system, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.
The invention has the following beneficial effects:
the invention provides a motor fault diagnosis method and system, which adopt a kernel density estimation method to calculate a fault probability density function, diagnose the fault type by comparing the probability density function of real-time operation data with the fault probability density functions of all types of faults, can find the abnormal condition of motor operation in time and carry out fault diagnosis, and have wide applicability, high accuracy, convenient use and easy implementation; the maintenance work can be conveniently and timely arranged, and the motor safety maintenance device has important significance in improving the safe operation of the motor.
The present invention will be described in further detail below with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a method for diagnosing electrode faults in accordance with a preferred embodiment of the present invention;
FIG. 2 is a waveform diagram of a fault detection result of a fault in a broken bar according to a preferred embodiment of the present invention;
FIG. 3 is a fault diagnosis result box diagram of the preferred embodiment of the present invention;
FIG. 4 is a waveform illustrating the detection of an air gap bearing fault in accordance with a preferred embodiment of the present invention;
FIG. 5 is a boxed view of the air gap bearing fault diagnosis result of the preferred embodiment of the present invention;
FIG. 6 is a waveform diagram of inter-turn short fault detection in accordance with a preferred embodiment of the present invention;
FIG. 7 is a boxed view of the inter-turn short fault diagnosis result of the preferred embodiment of the present invention;
FIG. 8 is a waveform illustrating the detection of an air gap eccentricity fault in accordance with a preferred embodiment of the present invention;
fig. 9 is a box-type view of the air gap eccentricity fault diagnosis result of the preferred embodiment of the present invention.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
Unless otherwise defined, all terms of art used hereinafter have the same meaning as commonly understood by one of ordinary skill in the art. The use of "first," "second," and similar terms in the description and in the claims of the present application do not denote any order, quantity, or importance, but rather the intention is to distinguish one element from another. Also, the use of the terms "a" or "an" and the like do not denote a limitation of quantity, but rather denote the presence of at least one.
Example 1
Referring to fig. 1, the present embodiment provides a motor fault diagnosis method, including the following steps:
s1: selecting normal operation data of a motor to be detected as a normal training data set, calculating a first detection statistic according to the normal training data set, and calculating a detection threshold according to the first detection statistic;
s2: selecting different types of fault data from historical fault operation data as a fault training data set, calculating second detection statistics according to the fault training data set, calculating a fault probability density function of the second detection statistics by adopting a kernel density estimation method, and constructing probability density function sets of all types of fault samples;
s3: selecting real-time operation data of the motor to be tested as a test data set, calculating third detection statistic according to the test data set, comparing the third detection statistic with a detection threshold, if the value of the third detection statistic is larger than the detection threshold, judging that a fault occurs, and entering S4; otherwise, the test data set is selected again until the motor is detected to be in fault;
s4: and calculating a fault density function of the third detection statistic by adopting a kernel density estimation method, constructing a probability density function set of the test data set, and diagnosing the fault type according to the distance between the probability density function set of the test data set and the probability density function sets of all types of fault samples in S2.
The motor fault diagnosis method can find the abnormal condition of the motor operation in time and carry out fault diagnosis, and has the advantages of wide applicability, high accuracy, convenient use and easy implementation; the maintenance work can be conveniently and timely arranged, and the motor safety maintenance device has important significance in improving the safe operation of the motor.
Specifically, the motor failure in the rail transit traction drive control system is taken as an example for explanation.
In the embodiment, the three-phase current of the motor is derived from the phase a, the phase b and the phase c of the rotor; the sampling time is 60000ms, which corresponds to 60000 samples; selecting a sample from the normal operation data to form a normal training data set, and calculating KL detection statistic (Kullback-Leibler divergence) of the normal operation data, wherein the normal operation data comprises parameters such as operation voltage, current, power, rotating speed and the like of the motor. In the embodiment, the KL detection statistic of the normal training data set is the first detection statistic, and a detection threshold value is obtained by a self-service method; selecting different types of fault samples from fault operation data to form a fault training data set, calculating KL detection statistics of the fault training data set, and calculating probability density functions of different types of faults by adopting a kernel density estimation method; selecting a sample from the field operation data to form a test data set, calculating KL detection statistics of the test data set, and comparing the KL detection statistics with a detection threshold to judge whether the motor fails; if the fault occurs, calculating KL divergence (distance) between the probability density function set of the test sample and the probability density function sets of all types of fault samples and box type graph areas of all types of faults; and judging the type of the current fault with the smallest area.
Preferably, the implementation steps of the invention are elaborated in combination with a motor air gap eccentricity fault in a traction drive control system fault injection benchmark software (TDCS-FIB V2.0) platform:
firstly, selecting a sample from normal operation data of a motor without air gap eccentric fault to form a normal training data set:
Figure BDA0001920072880000091
carrying out normalization pretreatment on the data set X to obtain a data set
Figure BDA0001920072880000092
Comprises the following steps:
Figure BDA0001920072880000093
calculating a data set by the above formula (1) and formula (2)
Figure BDA0001920072880000094
And the data set
Figure BDA0001920072880000095
The covariance matrix S.
Further, calculating KL detection statistics of the normal training sample set, specifically: from normal training data sets using a sliding window method
Figure BDA0001920072880000096
Extracting z-200 samples, and forming a kth normal training sample set as follows:
Figure BDA0001920072880000097
in the formula, k is 1, …, h, in this embodiment, the step length of each sliding window movement is set to 1, and h is N-z +1 normal training sample sets are extracted; then calculating a normal training sample set
Figure BDA0001920072880000101
Is a mean matrix of
Figure BDA0001920072880000102
Sum covariance matrix
Figure BDA0001920072880000103
Computing the kth Normal training sample set
Figure BDA0001920072880000104
KL detection statistic l (k) of (a), formula:
Figure BDA0001920072880000105
the h KL detection statistics of the normal training data set are obtained as follows: l (1), …, l (59801).
Note that the normal training sample set
Figure BDA0001920072880000106
The calculation method is specifically as follows: using sliding window method to extract data from data set
Figure BDA0001920072880000107
Extracting z samples to form the kth sample set
Figure BDA0001920072880000108
k is 1, …, h; in this embodiment, the step length of each sliding window movement is set to 1, and N-z +1 normal training sample sets are extracted altogether.
Further, a detection threshold J is calculatedth。
Collecting known air gap eccentric fault data to form a fault training data set; injecting air gap eccentric fault information based on the fault of the traction transmission control system, wherein the fault information comprises that the fault type is an air gap eccentric fault, the fault degree is 0.3, and the fault time is 40000ms, and the fault information is specifically shown in the following table 1:
TABLE 1 TDCS-FIB V2.0 platform Motor Fault
Figure BDA0001920072880000109
In this embodiment, the air gap eccentricity fault data sample is:
Figure BDA00019200728800001010
in the present embodiment, f is 1;
the fault data samples obtained by preprocessing are as follows:
Figure BDA00019200728800001011
preferably, the step length of each sliding window movement is set to 1, further, t-N-d +1 sliding window method acquisitions are performed on the data set, and each time d-200 fault samples are taken, then:
Figure BDA0001920072880000111
wherein q is 1, …, t;
calculating the mean matrix of the fault samples each time a sliding window is performed
Figure BDA0001920072880000112
Covariance matrix
Figure BDA0001920072880000113
Q-th fault training sample set for calculating air gap eccentricity fault
Figure BDA0001920072880000114
KL detection statistic l1(q) the formula:
Figure BDA0001920072880000115
after t sliding windows, t KL detection statistics of the air gap eccentric fault training sample set are obtained as follows: l1(1),…,l1(59801)；
Q fault training sample set for calculating air gap eccentric fault
Figure BDA0001920072880000116
KL detection statistic l1(q) fault probability density function P1(q) is:
Figure BDA0001920072880000117
probability density of failure function of air gap eccentricity is P1(1)，…，P1(59801). In the same way, the other 3 kinds of fault data of the motor are collected from the platform: broken bar fault, bearing fault, turn-to-turn short circuit fault. And respectively calculating the fault probability density function P of the 3 fault dataf(q)，f＝2,…,4；
From 59801 probability density functions P by adopting sliding window methodf(1),…,Pf(t), f is 1, …,4, sequentially extracting g is 100 data, and forming the r-th probability density function set corresponding to the f-th fault
Figure BDA0001920072880000118
The step length of each sliding window movement is 1, and r is extractedlT-g +1 f-th fault sample probability density function sets;
further, collecting motor operation data injected with air gap eccentric faults from real-time operation data of a traction drive control system fault injection reference software (TDCS-FIB V2.0) platform to form a test data set:
Figure BDA0001920072880000119
the method comprises the following steps of (1) collecting a test data set by a sliding window method for t-N-d +1 times, wherein d-200 samples are taken each time:
Figure BDA0001920072880000121
calculating the mean matrix of the corresponding data set each time a sliding window is performed
Figure BDA0001920072880000122
Sum covariance matrix
Figure BDA0001920072880000123
Computing the qth test sample set YqdKL detection statistic ly(q) is:
Figure BDA0001920072880000124
then the t KL detection statistics of the test sample set are: ly(1),…,ly(59801)。
And then judging the fault condition of the test data. Specifically, the average value of t KL detection statistics of the test sample set and a detection threshold J are calculatedthComparing; if the average value of the detection statistics of the test sample set is larger than the threshold value JthJudging the running state of the motor to be a fault;
after the occurrence of the fault is detected, calculating the q test sample set Y by adopting a nuclear density estimation methodqdKL detection statistic lyProbability density function P of (q)y(q) the formula:
Figure BDA0001920072880000125
then t KL detection statistics l corresponding to the test sample sety(q) a probability density function of Py(1)，…，Py(59801)。
From 59801 probability density functions P by adopting sliding window methody(1)，…，Py(59801) Sequentially extracting g-100 data to form the probability density function set of the r-th test sample
Figure BDA0001920072880000126
In this embodiment, the step length of each sliding window movement is 1, and r is extracted altogetherlT-g +1 sets of probability density functions.
Calculating the probability density function set of the r test sample
Figure BDA0001920072880000127
Probability density function set of class f fault sample
Figure BDA0001920072880000128
KL divergence (distance) between
Figure BDA0001920072880000129
Figure BDA00019200728800001210
In this embodiment, the KL divergence (distance) between the r-th test sample probability density function set and the 4-class fault sample probability density function set is:
Figure BDA00019200728800001211
r under f-type faultlThe individual KL divergence (distance) values are:
Figure BDA00019200728800001212
form it into a distance set
Figure BDA0001920072880000131
Then r islThe KL divergence (distance) set between the probability density function set of each test sample and the probability density function set of the n-type fault samples is respectively as follows:
Figure BDA0001920072880000132
and further, judging the fault type based on the area of the box type graph. Put KL divergence (distance) set DfIn the order from small to large
Figure BDA0001920072880000133
Calculating the lower quartile of each distance data
Figure BDA0001920072880000134
Median number
Figure BDA0001920072880000135
Upper quartile
Figure BDA0001920072880000136
Upper limit UmaxAnd lower limit UminAll D are plotted using the calculated values of equation 16f1, …, n; respectively calculate all corresponding DfArea S of box plotf：
Sf＝IQRf×W；(13)
In the formula, W is the base width, and in this embodiment, the value is 1. Specifically, when a fault occurs, the area calculation value of the box type diagram is shown in table 2 below, the waveform diagram of the fault detection result is shown in fig. 2, and the box type diagram of the fault diagnosis result is shown in fig. 3; when a bearing fault occurs, the area calculation value of the box type diagram is shown in the following table 3, the fault detection result waveform diagram is shown in fig. 4, and the fault diagnosis result box type diagram is shown in fig. 5; when a turn-to-turn short circuit fault occurs, the area calculation value of the box diagram is shown in the following table 4, the fault detection result waveform diagram is shown in fig. 6, and the fault diagnosis result box diagram is shown in fig. 7;
TABLE 2 area calculation values of box plots in the event of a broken bar fault
Figure BDA0001920072880000137
TABLE 3 area calculation values of box plot in case of bearing failure
Figure BDA0001920072880000138
TABLE 4 area calculation values of box plots in the event of turn-to-turn short circuit fault
Figure BDA0001920072880000141
Judging the type of the fault with the smallest area:
min(Sf)→f；
wherein min (S)f) → f denotes taking SfThe minimum value of the medium area corresponds to the fault, in the embodiment, the fault is the motor air gap eccentric fault, the waveform diagram of the fault detection result is shown in fig. 8, and the diagnosis result is shown in the chart box type diagram in fig. 9. In fig. 9, the box-shaped area corresponding to the motor air gap eccentricity fault is the smallest.
Example 2
In correspondence with the above method embodiments, the present embodiment provides a motor fault diagnosis system, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the steps of the above method when executing the computer program.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1.一种电机故障诊断方法，其特征在于，包括以下步骤：1. a motor fault diagnosis method, is characterized in that, comprises the following steps: S1：选取待测电机的正常运行数据作为正常训练数据集，根据所述正常训练数据集计算第一检测统计量，并根据所述第一检测统计量计算检测阈值；S1: Select the normal operation data of the motor to be tested as a normal training data set, calculate a first detection statistic according to the normal training data set, and calculate a detection threshold according to the first detection statistic; S2：从历史故障运行数据中选取不同类型的故障数据作为故障训练数据集，根据所述故障训练数据集计算第二检测统计量，采用核密度估计法计算所述第二检测统计量的故障概率密度函数，并构建所有类型故障样本的概率密度函数集；S2: Select different types of fault data from the historical fault operation data as the fault training data set, calculate the second detection statistic according to the fault training data set, and use the kernel density estimation method to calculate the failure probability of the second detection statistic density function, and construct a set of probability density functions for all types of failure samples; S3：选取待测电机的实时运行数据作为测试数据集，根据所述测试数据集计算第三检测统计量，将所述第三检测统计量与所述检测阈值进行比较，若所述第三检测统计量的值大于所述检测阈值，则判定发生故障，进入S4；反之，重新选取测试数据集，直至检测到电机发生故障；S3: Select the real-time operating data of the motor to be tested as a test data set, calculate a third detection statistic according to the test data set, and compare the third detection statistic with the detection threshold, if the third detection statistic If the value of the statistic is greater than the detection threshold, it is determined that a fault has occurred, and the process goes to S4; otherwise, the test data set is reselected until it is detected that the motor is faulty; S4：采用核密度估计法计算所述第三检测统计量的概率密度函数，并构建测试数据集的概率密度函数集，根据所述测试数据集的概率密度函数集和所述S2中所述所有类型故障样本的概率密度函数集的距离诊断故障类型。S4: Calculate the probability density function of the third detection statistic by using the kernel density estimation method, and construct the probability density function set of the test data set, according to the probability density function set of the test data set and all the The distance from the set of probability density functions of type fault samples to diagnose fault type. 2.根据权利要求1所述的电机故障诊断方法，其特征在于，所述S1具体包括以下步骤：2. The motor fault diagnosis method according to claim 1, wherein the S1 specifically comprises the following steps: S11：将正常训练数据集X表示为：S11: Represent the normal training dataset X as:
Figure FDA0003016582950000011
Figure FDA0003016582950000011
 式中，m为电机传感器个数，N为采样点数，x为根据时间序列采集到的电机运行数据样本，
Figure FDA0003016582950000012
为实数集；In the formula, m is the number of motor sensors, N is the number of sampling points, x is the motor operation data samples collected according to the time series,
Figure FDA0003016582950000012
is the set of real numbers; S12：对数据集X进行归一化预处理，得到数据集
Figure FDA0003016582950000013
为：S12: Perform normalization preprocessing on the dataset X to obtain a dataset
Figure FDA0003016582950000013
for:
Figure FDA0003016582950000014
Figure FDA0003016582950000014
 计算数据集
Figure FDA0003016582950000015
的均值矩阵μ，计算公式为：Computational dataset
Figure FDA0003016582950000015
The mean matrix μ of , the calculation formula is:
Figure FDA0003016582950000016
Figure FDA0003016582950000016
 式中，
Figure FDA0003016582950000021
In the formula,
Figure FDA0003016582950000021
 计算数据集
Figure FDA0003016582950000022
的协方差矩阵S，计算公式为：Computational dataset
Figure FDA0003016582950000022
The covariance matrix S of , the calculation formula is:
Figure FDA0003016582950000023
Figure FDA0003016582950000023
 采用滑窗法计算第一检测统计量，计算公式为：The sliding window method is used to calculate the first detection statistic, and the calculation formula is:
Figure FDA0003016582950000024
Figure FDA0003016582950000024
 式中，l(k)为第k个正常训练样本集
Figure FDA0003016582950000025
的第一检测统计量，
Figure FDA0003016582950000026
为第k个正常训练样本集
Figure FDA0003016582950000027
的均值矩阵，z为第k个正常训练样本集的样本个数，
Figure FDA0003016582950000028
为第k个正常训练样本集
Figure FDA0003016582950000029
的协方差矩阵，S-1为正常训练数据集的协方差矩阵的逆矩阵，||为二范数，tr()为矩阵的迹；In the formula, l(k) is the kth normal training sample set
Figure FDA0003016582950000025
The first detection statistic of ,
Figure FDA0003016582950000026
is the kth normal training sample set
Figure FDA0003016582950000027
The mean matrix of , z is the number of samples in the kth normal training sample set,
Figure FDA0003016582950000028
is the kth normal training sample set
Figure FDA0003016582950000029
The covariance matrix of , S -1 is the inverse matrix of the covariance matrix of the normal training data set, || is the second norm, and tr() is the trace of the matrix; 经过h次滑窗后，得到正常训练数据集的h个第一检测统计量为：l(1),…,l(h)；After h sliding windows, the h first detection statistics of the normal training data set are obtained as: l(1),...,l(h); S13：从正常训练数据集的h个第一检测统计量中随机抽取B个统计量，构成集合{l1,…,lB}，其中B＜h，对B个抽样样本按从小到大顺序重新排序为：l(1)＜l(2)＜…＜l(w)，令其中第w个最大的统计量值l(w)＝λ×α，其中α为允许的误报警率，λ为随机抽样次数，重复λ次抽样结束后，得到：
Figure FDA00030165829500000210
计算检测阈值Jth，计算公式为：S13: Randomly extract B statistics from the h first detection statistics of the normal training data set to form a set {l 1 ,...,l B }, where B<h, the B sampling samples are in ascending order Reordering is: l (1) <l (2) <...<l (w) , let the w-th largest statistic value l (w) = λ×α, where α is the allowable false alarm rate, λ is the number of random sampling, after repeating λ times of sampling, we get:
Figure FDA00030165829500000210
To calculate the detection threshold J th , the calculation formula is:
Figure FDA00030165829500000211
Figure FDA00030165829500000211
 3.根据权利要求2所述的电机故障诊断方法，其特征在于，所述S2具体包括以下步骤：3. The motor fault diagnosis method according to claim 2, wherein the S2 specifically comprises the following steps: S21：从历史故障运行数据中选取n类故障数据样本，其中第f类故障样本构成故障训练数据集Xf为：S21: Select n types of fault data samples from the historical fault operation data, wherein the f-th type of fault samples constitute the fault training data set X f is:
Figure FDA0003016582950000031
Figure FDA0003016582950000031
 式中，f＝1,…,n；In the formula, f=1,...,n; 对数据集Xf进行归一化处理，得到故障训练数据集
Figure FDA0003016582950000032
为：Normalize the data set X f to get the fault training data set
Figure FDA0003016582950000032
for:
Figure FDA0003016582950000033
Figure FDA0003016582950000033
 式中，f＝1,…,n；In the formula, f=1,...,n; S22：计算数据集
Figure FDA0003016582950000034
的均值矩阵
Figure FDA0003016582950000035
计算公式为：S22: Computational dataset
Figure FDA0003016582950000034
the mean matrix of
Figure FDA0003016582950000035
The calculation formula is:
Figure FDA0003016582950000036
Figure FDA0003016582950000036
 式中，
Figure FDA0003016582950000037
其中，d为从数据集
Figure FDA0003016582950000038
中提取的样本个数，q为第q个故障训练数据集，且i＝1,…,m,q＝1,…,t，t为通过滑窗法所采集的故障训练数据集的个数；In the formula,
Figure FDA0003016582950000037
Among them, d is the data set from
Figure FDA0003016582950000038
The number of samples extracted from, q is the qth fault training data set, and i=1,...,m, q=1,...,t, t is the number of fault training data sets collected by the sliding window method ; 计算数据集
Figure FDA0003016582950000039
的协方差矩阵
Figure FDA00030165829500000310
计算公式为：Computational dataset
Figure FDA0003016582950000039
The covariance matrix of
Figure FDA00030165829500000310
The calculation formula is:
Figure FDA00030165829500000311
Figure FDA00030165829500000311
 采用滑窗法计算第f类故障的第q个故障训练样本集
Figure FDA00030165829500000312
的第二检测统计量lf(q)，计算公式为：Using the sliding window method to calculate the qth fault training sample set of the fth fault
Figure FDA00030165829500000312
The second detection statistic l f (q) of , the calculation formula is:
Figure FDA00030165829500000313
Figure FDA00030165829500000313
 经过t次滑窗后，得到第f类故障训练样本集的t个第二检测统计量为：lf(1),…,lf(t)；After t sliding windows, the t second detection statistics of the f-th fault training sample set are obtained as: l f (1),...,l f (t); S23：采用核密度估计法计算第f类故障第q个故障训练样本集
Figure FDA00030165829500000314
的第二检测统计量lf(q)的故障概率密度函数Pf(q)，计算公式为：S23: Use the kernel density estimation method to calculate the qth fault training sample set of the fth type fault
Figure FDA00030165829500000314
The failure probability density function P f (q) of the second detection statistic l f (q) of , the calculation formula is:
Figure FDA0003016582950000041
Figure FDA0003016582950000041
 对应第f类故障的t个第二检测统计量的概率密度函数为Pf(1)，…，Pf(t)；The probability density function of the t second detection statistics corresponding to the f-th fault is P f (1), ..., P f (t); 采用滑窗法从t个概率密度函数Pf(1),…,Pf(t)中顺序提取g个数据，构成对应第f类故障的第r个概率密度函数集为：The sliding window method is used to sequentially extract g data from t probability density functions P f (1),...,P f (t), and the rth probability density function set corresponding to the f-th type of fault is:
Figure FDA0003016582950000042
Figure FDA0003016582950000042
 式中，r＝1,…,rl，rl为第f类故障样本概率密度函数集的个数。In the formula, r=1,...,r l , and rl is the number of probability density function sets of the f- th fault samples. 4.根据权利要求3所述的电机故障诊断方法，其特征在于，所述S3中根据所述故障训练数据集计算第三检测统计量具体包括以下步骤：4. The motor fault diagnosis method according to claim 3, wherein the calculation of the third detection statistic according to the fault training data set in the S3 specifically comprises the following steps: S31：选取待测电机的实时运行数据作为测试数据集Y为：S31: Select the real-time running data of the motor to be tested as the test data set Y:
Figure FDA0003016582950000043
Figure FDA0003016582950000043
 对测试数据集Y进行归一化预处理，得到数据集
Figure FDA0003016582950000044
为：Perform normalization preprocessing on the test data set Y to get the data set
Figure FDA0003016582950000044
for:
Figure FDA0003016582950000045
Figure FDA0003016582950000045
 计算测试数据集
Figure FDA0003016582950000046
的均值矩阵
Figure FDA0003016582950000047
计算公式为：Compute the test dataset
Figure FDA0003016582950000046
the mean matrix of
Figure FDA0003016582950000047
The calculation formula is:
Figure FDA0003016582950000048
Figure FDA0003016582950000048
 式中，
Figure FDA0003016582950000049
且i＝1,…,m,q＝1,…,t，其中，d为从数据集Y中提取的样本个数，q为第q个测试训练数据集；In the formula,
Figure FDA0003016582950000049
And i=1,...,m,q=1,...,t, where d is the number of samples extracted from the data set Y, and q is the qth test training data set; 计算测试数据集Y的协方差矩阵
Figure FDA00030165829500000410
计算公式为：Calculate the covariance matrix of the test dataset Y
Figure FDA00030165829500000410
The calculation formula is:
Figure FDA0003016582950000051
Figure FDA0003016582950000051
 采用滑窗法计算第q个测试样本集Yqd的第三检测统计量ly(q)，计算公式为：The sliding window method is used to calculate the third detection statistic ly (q) of the qth test sample set Y qd , and the calculation formula is:
Figure FDA0003016582950000052
Figure FDA0003016582950000052
 经过t次滑窗后，得到测试样本集的t个第三检测统计量为：ly(1),…,ly(t)。After t sliding windows, the t third detection statistics of the test sample set are obtained as: ly (1),..., ly (t). 5.根据权利要求1所述的电机故障诊断方法，其特征在于，所述S1中，所述正常运行数据包括：电机的运行电压、电流、功率以及转速。5 . The method for diagnosing a motor fault according to claim 1 , wherein, in the S1 , the normal operation data includes: the operating voltage, current, power, and rotational speed of the motor. 6 . 6.根据权利要求4所述的电机故障诊断方法，其特征在于，所述S4具体包括以下步骤：6. The motor fault diagnosis method according to claim 4, wherein the S4 specifically comprises the following steps: S41：采用核密度估计法计算第q个测试样本集Yqd的第三检测统计量ly(q)的概率密度函数Py(q)，计算公式为：S41: Using the kernel density estimation method to calculate the probability density function P y (q) of the third detection statistic ly (q) of the qth test sample set Y qd , the calculation formula is:
Figure FDA0003016582950000053
Figure FDA0003016582950000053
 式中，q＝1,…,t；In the formula, q=1,...,t; 对应测试样本集的t个第三检测统计量ly(q)的概率密度函数为Py(1)，…，Py(t)；The probability density function of the t third detection statistics ly (q) corresponding to the test sample set is P y (1), ..., P y (t); 采用滑窗法从t个概率密度函数Py(1)，…，Py(t)中顺序提取g个数据，构成第r个测试样本概率密度函数集为：The sliding window method is used to sequentially extract g data from t probability density functions P y (1), ..., P y (t), and form the rth test sample probability density function set as:
Figure FDA0003016582950000054
Figure FDA0003016582950000054
 式中，r＝1,…,rl；In the formula, r=1,...,r l ; 计算第r个测试样本概率密度函数集与第f类故障样本概率密度函数集之间的距离，计算公式为：Calculate the distance between the probability density function set of the rth test sample and the probability density function set of the f-th fault sample. The calculation formula is:
Figure FDA0003016582950000055
Figure FDA0003016582950000055
 第r个测试样本概率密度函数集与n类故障样本概率密度函数集间的距离为：
Figure FDA0003016582950000056
第f类故障下的rl个距离值为：
Figure FDA0003016582950000057
将其构成距离集
Figure FDA0003016582950000061
则rl个测试样本概率密度函数集与n类故障样本概率密度函数集间的距离的集合分别为：The distance between the probability density function set of the rth test sample and the probability density function set of the n-type fault samples is:
Figure FDA0003016582950000056
The r l distance values under the f-th fault are:
Figure FDA0003016582950000057
form it into a distance set
Figure FDA0003016582950000061
Then the sets of distances between r l test sample probability density function sets and n-type fault sample probability density function sets are:
Figure FDA0003016582950000062
Figure FDA0003016582950000062
 将上述距离的集合Df中的距离数据从小到大顺序重新排列为
Figure FDA0003016582950000063
采用箱型图面积计算方法计算每一个距离数据的下四分位数
Figure FDA0003016582950000064
中位数
Figure FDA0003016582950000065
上四分位数
Figure FDA0003016582950000066
上限Umax和下限Umin为：Rearrange the distance data in the set D f of the above distances from small to large as
Figure FDA0003016582950000063
Calculate the lower quartile of each distance data using the box plot area calculation method
Figure FDA0003016582950000064
median
Figure FDA0003016582950000065
upper quartile
Figure FDA0003016582950000066
The upper limit U max and the lower limit U min are:
Figure FDA0003016582950000067
Figure FDA0003016582950000067
 式中
Figure FDA0003016582950000068
表示向上取正，
Figure FDA0003016582950000069
为取
Figure FDA00030165829500000610
集中对应位置·处的数据；in the formula
Figure FDA0003016582950000068
means to be positive upward,
Figure FDA0003016582950000069
to take
Figure FDA00030165829500000610
Centralize the data at the corresponding location; 用公式(20)计算值画出所有Df,f＝1,…,n的箱型图；分别计算对应所有Df箱型图的面积Sf：Use the calculated value of formula (20) to draw all the box plots of D f , f=1,...,n; respectively calculate the area S f corresponding to all D f box plots: Sf＝IQRf×W； (23)S f =IQR f ×W; (23) 式中，W为底边宽；In the formula, W is the width of the bottom edge; S42：判断所有Df箱型图中面积最小的箱型图，并将该箱型图对应的故障类型判定为待测电机所发生故障类型。S42: Determine the box diagram with the smallest area in all D f box diagrams, and determine the fault type corresponding to the box diagram as the fault type of the motor to be tested. 7.根据权利要求4所述的电机故障诊断方法，其特征在于，所述第一检测统计量、所述第二检测统计量以及所述第三检测统计量的类型都为KL检测统计量。7 . The motor fault diagnosis method according to claim 4 , wherein the types of the first detection statistic, the second detection statistic and the third detection statistic are all KL detection statistics. 8 . 8.根据权利要求6所述的电机故障诊断方法，其特征在于，所述S41中，第r个测试样本概率密度函数集与n类故障样本概率密度函数集间的距离为第r个测试样本概率密度函数集与n类故障样本概率密度函数集间的KL散度。8 . The motor fault diagnosis method according to claim 6 , wherein, in the step S41 , the distance between the rth test sample probability density function set and the n-type fault sample probability density function set is the rth test sample. 9 . The KL divergence between the probability density function set and the probability density function set of n-type fault samples. 9.一种电机故障诊断系统，包括存储器、处理器以及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现上述权利要求1至8任一所述方法的步骤。9. A motor fault diagnosis system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements claims 1 to 1 when the processor executes the computer program 8 any of the steps of the method.
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