Motor fault diagnosis method and system based on multi-sensor data fusion and medium

Abstract

The embodiment of the invention discloses a motor fault diagnosis method and system based on multi-sensor data fusion and a medium. The method comprises the following steps: acquiring initial signal data of each sensor; performing noise reduction processing on the initial signal data through singular value decomposition; extracting an intrinsic mode function component of the denoised initial signal data, and obtaining reconstruction data according to the intrinsic mode function component; constructing a back-propagation neural network based on the reconstructed data, introducing crossover probability and mutation probability in a genetic algorithm to correct the weight and threshold of the back-propagation neural network, and determining a hybrid network; obtaining the fault diagnosis probability of each sensor according to the hybrid network; determining an evidence reliability factor according to the inherent reliability coefficient of each sensor and the inherent reliability coefficient of the hybrid network; the fault diagnosis probability of each sensor is corrected according to the evidence reliability factor, the evidence probability is determined, motor fault prediction is carried out through the evidence probability, and the accuracy and credibility of overall diagnosis are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of motor fault diagnosis, and in particular to a motor fault diagnosis method, system and medium based on multi-sensor data fusion. Background Art

[0002] With the rapid development of industrialization, informatization and intelligence, the global industrial automation process is also advancing, and various equipment are widely used in various important fields. As the core component of equipment operation, the demand for electric motors is increasing, and the product quality requirements are getting higher and higher. Its reliability directly affects and even determines the stability and continuity of the entire production process, and it plays an irreplaceable and important role in industrial production. In the daily operation of motors, they are often prone to failure due to improper operation by users, poor maintenance, aging of internal insulation, etc. Once a failure occurs, it stops running, which will directly destroy the continuity of the production process, and seriously endanger the life and health of on-site workers, and easily cause serious safety accidents and huge economic losses. Therefore, the diagnosis of motor failures is very necessary and has strong economic benefits and practical significance.

[0003] Traditional motor fault diagnosis mostly uses data from a single sensor for fault diagnosis. Relying on a single sensor for fault diagnosis has reliability risks, and a single sensor can only provide limited information and cannot fully reflect the operating status of the motor. In fact, when a motor fails, the diagnostic results of sensors of different types and different parts will have certain differences. Combining data from different sensors for joint diagnosis can improve the reliability of the diagnostic results to a considerable extent. Summary of the invention

[0004] Based on this, it is necessary to propose a motor fault diagnosis method, system and medium based on multi-sensor data fusion to address the above problems.

[0005] A motor fault diagnosis method based on multi-sensor data fusion, the method comprising:

[0006] Get the initial signal data of each sensor.

[0007] The initial signal data is denoised by singular value decomposition.

[0008] Extracting the intrinsic mode function components of the initial signal data after noise reduction, and acquiring reconstructed data according to the intrinsic mode function components.

[0009] A back propagation neural network is constructed based on the reconstructed data, and the crossover probability and mutation probability in the genetic algorithm are introduced to correct the weights and thresholds of the back propagation neural network to determine the hybrid network.

[0010] The fault diagnosis probability of each sensor is obtained according to the hybrid network.

[0011] An evidence reliability factor is determined according to the inherent reliability coefficients of the sensors and the inherent reliability coefficient of the hybrid network.

[0012] The fault diagnosis probability of each sensor is corrected according to the evidence reliability factor, the evidence probability is determined, and the motor fault prediction is performed based on the evidence probability.

[0013] The denoising process of the initial signal data by singular value decomposition specifically includes:

[0014] The Hankel matrix of the initial signal data is determined by phase space reconstruction, and the number of rows and columns of the Hankel matrix is ​​determined according to the number of signal samples of the initial signal data.

[0015] Performing singular value decomposition on the Hankel matrix to obtain singular values, and determining the degree of change between adjacent singular values.

[0016] It is determined that the degree of change between the current singular value and the next singular value has a sudden change, and the singular value is the dividing point.

[0017] All singular values ​​before the demarcation point are retained, and after the remaining singular values ​​are set to zero, a new Hankel matrix is ​​obtained according to the inverse process of the singular value decomposition.

[0018] According to the inverse process of the phase space reconstruction, the new Hankel matrix is ​​converted into the initial signal data after noise reduction.

[0019] The determining of the Hankel matrix of the initial signal by phase space reconstruction, and determining the number of rows and columns of the Hankel matrix according to the number of signal samples of the initial signal, specifically includes:

[0020] pass Determine the Hankel matrix of the initial signal data, where x1, x2, ..., x N is the initial signal data, m is the number of rows of the Hankel matrix, n is the number of columns of the Hankel matrix, D m×n is the signal subspace without noise interference, W m×n is the noise signal subspace.

[0021] according to The number of rows of the Hankel matrix is ​​determined, where N is the number of signal samples.

[0022] The number of columns of the Hankel matrix is ​​determined according to n=N+1-m.

[0023] The step of performing singular value decomposition on the Hankel matrix to obtain singular values ​​and determining the degree of change between each singular value and the next singular value specifically includes:

[0024] according to The Hankel matrix is ​​subjected to singular value decomposition, wherein m is the number of rows of the Hankel matrix, n is the number of columns of the Hankel matrix, U and V are both orthogonal matrices, and Σ is a diagonal matrix.

[0025] according to Get the singular values, where S = diag(σ1,σ2,…,σ r ), r is the rank of the Hankel matrix, r∈[1,m], σ i is the i-th singular value in the Hankel matrix, and σ i ≥σ i+1 , i∈[1,r-1].

[0026] according to Determine how much each singular value varies from the next, where a i is the i-th singular value, a max is the maximum value among the singular values, a min is the minimum value among the singular values.

[0027] The step of extracting the intrinsic mode function components of the initial signal data after noise reduction and obtaining the reconstructed data according to the intrinsic mode function components specifically includes:

[0028] Random white noise with zero mean and equal variance is added to the initial signal data after noise reduction to obtain a signal group after white noise is added.

[0029] Performing empirical mode decomposition on each signal in the signal group after adding white noise, extracting several groups of intrinsic mode function components and residuals.

[0030] An intrinsic mode function component matrix and a residual matrix are determined according to the plurality of groups of intrinsic mode function components and residuals.

[0031] The intrinsic mode function component means and the residual means are calculated column by column for the intrinsic mode function component matrix and the residual matrix respectively.

[0032] Reconstructed data is obtained according to the intrinsic mode function component mean and the residual mean.

[0033] The step of obtaining the reconstructed data according to the intrinsic mode function component mean and the residual mean specifically includes:

[0034] according to Get the reconstructed data, where C iis the mean value corresponding to each column in the intrinsic mode function component matrix, i∈[1,w], w is the number of intrinsic mode function components, v is the number of components of random white noise added, R is the residual mean, r iw is the residual of the i-th column in the residual matrix.

[0035] The step of constructing a back propagation neural network based on the reconstructed data, correcting the weights and thresholds of the back propagation neural network by crossover probability and mutation probability, and determining a hybrid network specifically includes:

[0036] The number of nodes in the input layer, hidden layer and output layer of the back-propagation neural network is determined according to the reconstructed data, and the back-propagation neural network is constructed.

[0037] The population size and evolution times of the particle swarm, the initial position and initial velocity of each particle, the inertia weight and the acceleration factor are set. Each particle represents a set of weights and thresholds of the back propagation neural network.

[0038] According to the inertia weight and the acceleration factor, the initial speed and initial position of each particle are updated until a preset population size is reached, and the optimal speed and optimal position of each particle are obtained.

[0039] The optimal position of particles is changed according to the crossover probability and mutation probability.

[0040] According to the optimal position of the changed particle, the individual optimal fitness and the population optimal fitness of the particle group are determined to obtain the optimal particle group.

[0041] The weights and thresholds of the back propagation neural network are modified according to the optimal particle swarm until a preset number of evolutions is reached to determine a hybrid network.

[0042] The method of correcting the fault diagnosis probability of each sensor according to the evidence reliability factor, determining the evidence probability, and predicting the motor fault according to the evidence probability specifically includes:

[0043] according to m ij ′ determines the probability of evidence, where m ij ′=β i m ij , m ij is the fault diagnosis probability of each sensor in the jth case of the i-th evidence, i∈[1,L′], j∈[1,N], L′ is the number of independent evidences, N′ is the number of evaluation levels, m ij ′ is the optimized fault diagnosis probability of each sensor, and Θ is the completely uncertain situation in the fault situation.

[0044] A motor fault diagnosis system based on multi-sensor data fusion, the system comprising:

[0045] The initial signal data acquisition module is used to acquire the initial signal data of each sensor.

[0046] The signal denoising module is used to perform denoising on the initial signal data through singular value decomposition.

[0047] The reconstructed data acquisition module is used to extract the intrinsic mode function components of the initial signal data and acquire the reconstructed data according to the intrinsic mode function components.

[0048] The hybrid network determination module is used to construct a back propagation neural network based on the reconstructed data, introduce the crossover probability and mutation probability in the genetic algorithm to correct the weights and thresholds of the back propagation neural network, and determine the hybrid network.

[0049] The module for acquiring the fault diagnosis probability of each sensor is used to acquire the fault diagnosis probability of each sensor according to the hybrid network.

[0050] The evidence reliability factor determination module is used to determine the evidence reliability factor according to the inherent reliability coefficients of the sensors and the inherent reliability coefficient of the hybrid network.

[0051] The motor fault prediction module is used to correct the fault diagnosis probability of each sensor according to the evidence reliability factor, determine the evidence probability, and predict the motor fault based on the evidence probability.

[0052] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the steps of the method described above.

[0053] The embodiments of the present invention have the following beneficial effects:

[0054] The present invention improves the accuracy and robustness of fault diagnosis based on the initial signal data provided by multiple sensors. By extracting the intrinsic mode function components through the empirical mode decomposition method, the complex signal can be decomposed into a series of intrinsic mode function components with different frequencies and amplitudes, which helps to reveal the intrinsic local characteristics of the signal, and the intrinsic mode function extraction process helps to remove the noise components in the signal, improve the signal quality, and make the subsequent analysis more accurate. The reconstructed data based on the intrinsic mode function components is closer to the true form of the signal, which helps to more accurately reflect the operating status of the equipment. Further, the back propagation neural network constructed according to the reconstructed data has a strong nonlinear mapping ability, can handle complex nonlinear relationships, and is suitable for scenes such as fault diagnosis. The weights and thresholds of the neural network are corrected in combination with the crossover probability and the mutation probability, which can optimize the network structure, improve the generalization ability and diagnostic accuracy of the network, and obtain a hybrid network. The hybrid network can output the fault diagnosis probability of each sensor, providing a quantitative basis for subsequent fault prediction and decision-making. Furthermore, the reliability coefficients of sensors and hybrid networks are comprehensively considered, and reasonable weights are assigned to different sensors and diagnostic results by determining the evidence reliability factor, thereby improving the accuracy and credibility of the overall diagnosis. The fault diagnosis probability of each sensor is corrected according to the evidence reliability factor, and the evidence probability is determined. Motor faults are predicted based on the evidence probability, and potential faults are discovered in advance, providing strong support for equipment maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] in:

[0057] Figure 1 A flow chart of an embodiment of a motor fault diagnosis method based on multi-sensor data fusion provided by the present invention;

[0058] Figure 2 A flow chart of another embodiment of the motor fault diagnosis method based on multi-sensor data fusion provided by the present invention;

[0059] Figure 3 A flow chart of another embodiment of the motor fault diagnosis method based on multi-sensor data fusion provided by the present invention;

[0060] Figure 4 A schematic diagram of a flow chart of an embodiment of a motor fault diagnosis system based on multi-sensor data fusion provided by the present invention;

[0061] Figure 5 A schematic structural diagram of an embodiment of the medium provided by the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] like Figure 1 As shown, Figure 1 A flow chart of an embodiment of a motor fault diagnosis method based on multi-sensor data fusion provided by the present invention. A motor fault diagnosis method based on multi-sensor data fusion, the method comprising:

[0064] S101: Acquire initial signal data of each sensor.

[0065] S102: Performing noise reduction processing on the initial signal data by singular value decomposition.

[0066] Exemplarily, the initial signal data is collected by multiple data collection sensors and stored. Further, the initial signal data is subjected to noise reduction processing by singular value decomposition.

[0067] Specifically, for any initial signal data containing noise X(N) = {x1, x2, ..., x N}, construct the m×n Hankel matrix through phase space reconstruction:

[0068]

[0069] Among them, x1,x2,…,x N is the initial signal data, m is the number of rows of the Hankel matrix, n is the number of columns of the Hankel matrix, D m×n is the signal subspace without noise interference, W m×n is the noise signal subspace;

[0070] The number of rows and columns of the Hankel matrix is ​​determined according to the number of signal samples of the initial signal data, as shown in the following formula:

[0071]

[0072] Wherein, N is the number of signal samples, N=m+n-1.

[0073] Furthermore, the constructed Hankel matrix is ​​subjected to singular value decomposition, as shown in the following formula:

[0074]

[0075] Among them, U is an orthogonal matrix, V is the transpose of the orthogonal matrix, and Σ is a diagonal matrix. At this time:

[0076]

[0077] S=diag(σ1,σ2,…,σ r )(1≤r≤m);

[0078] Where r is the rank of the Hankel matrix H, σ i is the i-th singular value of the matrix H, and σ i ≥σ i+1 (i=1,2,…,r-1).

[0079] At the same time, determine the degree of change b between adjacent singular values i , as shown below:

[0080]

[0081] Among them, a i is the i-th singular value, a max is the maximum value among the singular values, a min is the minimum value among the singular values. If the degree of change between the current singular value and the next singular value changes suddenly, it indicates that this point is the dividing point between the noise-free interference signal and the noise signal. All singular values ​​before the dividing point are retained, and the singular values ​​after the dividing point are set to 0. The new Hankel matrix is ​​obtained according to the inverse process of singular value decomposition, and the new Hankel matrix is ​​converted into the initial signal data after noise reduction according to the inverse process of phase space reconstruction.

[0082] S103: extracting the intrinsic mode function components of the initial signal data after noise reduction, and obtaining reconstructed data according to the intrinsic mode function components.

[0083] For example, random white noise {n1(t), n2(t), ..., n v (t)}, then the initial signal data after adding random noise becomes a set of signal groups {x1(t),x2(t),…,x v (t)}, the relationship between the signal containing random white noise and the initial signal data after denoising without white noise is as follows:

[0084]

[0085] Among them, n v is the vth random white noise, xv is the initial signal data after denoising after adding white noise for the vth time, and v is the number of components of random white noise added.

[0086] Furthermore, for the signal group {x1(t),x2(t),…,x v (t)} for each signal x i (t)(i=1,2,…,v) are decomposed by EMD respectively to obtain v groups of intrinsic mode function components {[c 11 ,c 12 ,…,c 1w ],[c 21 ,c 22 ,…,c 2w ],…[c v1 ,c v2 ,…,c vm ]} (denoted as C) and v group residual [r 1w ,r 2w ,…,r vw ] T Denoted as (R), the eigenmode function component matrix is ​​as follows:

[0087]

[0088] Wherein, v is the number of rows of the intrinsic mode function component matrix, that is, the number of components of the added random white noise, and w is the number of columns of the intrinsic mode function component matrix, that is, the number of intrinsic mode function components.

[0089] The residual matrix is ​​shown as follows:

[0090]

[0091] Where v is the number of rows in the residual matrix, which is the same as the number of rows in the intrinsic mode function component matrix, and w is the number of columns in the residual matrix, which is the same as the number of columns in the intrinsic mode function component matrix.

[0092] Furthermore, the intrinsic mode function component means and the residual means are calculated column by column for the intrinsic mode function component matrix and the residual matrix, and the reconstructed data is obtained according to the intrinsic mode function component means and the residual means. Specifically, the matrix C is calculated column by column to obtain the means corresponding to each column of the v groups of intrinsic mode function components [C1, C2, …, C w ] is shown in the following formula:

[0093]

[0094] The mean R of the m groups of residuals is as follows:

[0095]

[0096] Then the reconstructed data x'(t) is as follows:

[0097]

[0098] S104: constructing a back propagation neural network based on the reconstructed data, introducing the crossover probability and mutation probability in the genetic algorithm to correct the weights and thresholds of the back propagation neural network, and determining the hybrid network.

[0099] Exemplarily, the number of nodes in the input layer, hidden layer and output layer of the back-propagation neural network is determined according to the reconstructed data, and the back-propagation neural network is constructed; the population size and number of evolutions of the particle swarm, the initial position and initial velocity of each particle, the inertia weight and the acceleration factor are set, and each particle represents a set of weights and thresholds of the back-propagation neural network; according to the inertia weight and the acceleration factor, the initial velocity and initial position of each particle are updated until the preset population size is reached, and the optimal velocity and optimal position of each particle are obtained; the optimal position of the particle is changed according to the crossover probability and the mutation probability; according to the optimal position of the changed particle, the individual optimal fitness and the population optimal fitness of the particle swarm are determined to obtain the optimal particle swarm; according to the optimal particle swarm, the weights and thresholds of the back-propagation neural network are corrected until the preset number of evolutions is reached, and the hybrid network is determined.

[0100] Specifically, define the inertia weight ω(k):

[0101] ω(k)=ω start -(ω start -ω end )×k / T max ;

[0102] Among them, ω start is the initial inertia weight, ω end is the inertia weight of the maximum number of iterations, k is the current number of iterations, T max is the maximum number of iterations.

[0103] Define the particle acceleration factors c1 and c2:

[0104] c1=c2=2.05.

[0105] S105: Obtaining the fault diagnosis probability of each sensor according to the hybrid network.

[0106] Exemplarily, the sensor data to be diagnosed is input into a trained hybrid network, and the fault diagnosis probability of each sensor is calculated through the forward propagation process of the hybrid network.

[0107] S106: Determine the evidence reliability factor according to the inherent reliability coefficient of each sensor and the inherent reliability coefficient of the hybrid network.

[0108] Exemplarily, the calculation of the evidence reliability factor is mainly related to two factors: the inherent reliability coefficient g of each sensor itself and the reliability coefficient s of the hybrid network.

[0109] It should be noted that the inherent reliability coefficient of each sensor is generally determined by experience and is not limited here. The reliability coefficient of the hybrid network generally refers to the reliability of the fault diagnosis probability assignment of each sensor calculated after the data is processed by the hybrid network. In the calculation of this method, it is mainly related to the accuracy of the hybrid network. Its accuracy is denoted as tru, and the relationship between the evidence reliability factor and the two reliability coefficients is defined as shown in the following formula:

[0110] β i =g i ×s i ;

[0111]

[0112] Among them, β i is the reliability factor of evidence, g i is the inherent reliability coefficient of the multi-sensor, s i is the reliability coefficient of the hybrid network, e is the base of the natural logarithm, tru i is the credibility of the hybrid network used, which mainly refers to the accuracy in this method, where 0≤g i ≤1,0≤tru i ≤1.

[0113] S107: Correct the fault diagnosis probability of each sensor according to the evidence reliability factor, determine the evidence probability, and predict the motor fault based on the evidence probability.

[0114] For example, suppose an evaluation result is composed of L′ pieces of independent evidence m i (i=1,2,…,L′) is evaluated, and the recognition framework is composed of N′ evaluation levels θ n (n=1,2,…,N′), and redistribute the probability of each fault state of the original evidence according to the evidence reliability factor, and correct the fault diagnosis probability of each sensor. The principle of defining the evidence reliability factor to optimize the basic probability assignment is shown in the following formula:

[0115] m ij ′=β i m ij (i=1,2,…,L′,j=1,2,…,N′);

[0116] Among them, m ijis the fault diagnosis probability of each sensor in the jth case of the i-th evidence, i∈[1,L′], j∈[1,N], L′ is the number of independent evidences, N′ is the number of evaluation levels, m ij ′ is the optimized fault diagnosis probability of each sensor, and Θ is the completely uncertain situation in the fault situation. Then the global uncertainty probability in the allocated evidence, that is, the evidence probability, is as follows:

[0117]

[0118] Among them, m iΘ ' is the probability of evidence.

[0119] Motor fault prediction is performed through evidence probability. However, when the evidence reliability factor is higher, the corresponding probability assignment of the fusion result is more concentrated, and the accuracy of the prediction result is higher; when the evidence reliability factor becomes lower, the peak probability of the corresponding fault state becomes lower, the global uncertainty increases, and the accuracy of the prediction result becomes lower.

[0120] It can be seen from the above description that the present invention improves the accuracy and robustness of fault diagnosis based on the initial signal data provided by multiple sensors. By extracting the intrinsic mode function components through the empirical mode decomposition method, the complex signal can be decomposed into a series of intrinsic mode function components with different frequencies and amplitudes, which helps to reveal the intrinsic local characteristics of the signal, and the intrinsic mode function extraction process helps to remove the noise components in the signal, improve the signal quality, and make the subsequent analysis more accurate. The reconstructed data based on the intrinsic mode function components is closer to the true form of the signal, which helps to more accurately reflect the operating status of the equipment. Further, the back propagation neural network constructed according to the reconstructed data has a strong nonlinear mapping ability, can handle complex nonlinear relationships, and is suitable for scenes such as fault diagnosis. The weights and thresholds of the neural network are corrected in combination with the crossover probability and the mutation probability, which can optimize the network structure, improve the generalization ability and diagnostic accuracy of the network, and obtain a hybrid network. The hybrid network can output the fault diagnosis probability of each sensor, providing a quantitative basis for subsequent fault prediction and decision-making. Furthermore, the reliability coefficients of sensors and hybrid networks are comprehensively considered, and reasonable weights are assigned to different sensors and diagnostic results by determining the evidence reliability factor, thereby improving the accuracy and credibility of the overall diagnosis. The fault diagnosis probability of each sensor is corrected according to the evidence reliability factor, and the evidence probability is determined. Motor faults are predicted based on the evidence probability, and potential faults are discovered in advance, providing strong support for equipment maintenance.

[0121] like Figure 2 As shown, Figure 2 A flow chart of another embodiment of a motor fault diagnosis method based on multi-sensor data fusion provided by the present invention. A motor fault diagnosis method based on multi-sensor data fusion, the method comprising:

[0122] S201: Acquire initial signal data of each sensor.

[0123] S202: Determine the Hankel matrix of the initial signal data through phase space reconstruction, and determine the number of rows and columns of the Hankel matrix according to the number of signal samples of the initial signal data.

[0124] Exemplarily, specifically, for any initial signal data containing noise X(N)={x1,x2,…,x N}, construct the m×n Hankel matrix through phase space reconstruction:

[0125]

[0126] Among them, x1,x2,…,x N is the initial signal data, m is the number of rows of the Hankel matrix, n is the number of columns of the Hankel matrix, D m×n is the signal subspace without noise interference, W m×n is the noise signal subspace;

[0127] The number of rows and columns of the Hankel matrix is ​​determined according to the number of signal samples of the initial signal data, as shown in the following formula:

[0128] According to the basic inequality principle, when the number of rows and columns are equal or closest, the product of the two is the largest, which satisfies the condition that the product of the number of rows and columns of the Hankel matrix is ​​the largest, and thus the signal and noise can be fully separated. The maximum product of the number of rows and columns depends mainly on the number of signal samples used. The number of rows and columns of the Hankel matrix can be determined according to the number of sampling points of the signal, that is,

[0129]

[0130] Wherein, N is the number of signal samples, n=N+1-m.

[0131] S203: Perform singular value decomposition on the Hankel matrix, obtain singular values, and determine the degree of change between adjacent singular values.

[0132] Exemplarily, the constructed Hankel matrix is ​​subjected to singular value decomposition, as shown in the following formula:

[0133]

[0134] Among them, U is an orthogonal matrix, V is the transpose of the orthogonal matrix, and Σ is a diagonal matrix. At this time:

[0135]

[0136] S=diag(σ1,σ2,…,σr )(1≤r≤m);

[0137] Where r is the rank of the Hankel matrix H, σ i is the i-th singular value of the matrix H, and σ i ≥σ i+1 (i=1,2,…,r-1).

[0138] At the same time, determine the degree of change b between adjacent singular values i , as shown below:

[0139]

[0140] Among them, a i is the i-th singular value, a max is the maximum value among the singular values, a min is the minimum value among the singular values.

[0141] S204: Determine that a sudden change occurs between the current singular value and the next singular value, and the singular value is a dividing point.

[0142] S205: retain all singular values ​​before the demarcation point, set the remaining singular values ​​to zero, and obtain a new Hankel matrix according to the inverse process of singular value decomposition.

[0143] S206: According to the inverse process of phase space reconstruction, the new Hankel matrix is ​​converted into the initial signal data after noise reduction.

[0144] Exemplarily, if the degree of change between the current singular value and the next singular value changes suddenly, it indicates that the point is the dividing point between the noise-free interference signal and the noise signal. All singular values ​​before the dividing point are retained, and the singular values ​​after the dividing point are set to 0. Then, a new Hankel matrix is ​​obtained according to the inverse process of singular value decomposition, and the new Hankel matrix is ​​converted into the initial signal data after noise reduction according to the inverse process of phase space reconstruction.

[0145] S207: adding random white noise with zero mean and equal variance to the initial signal data after noise reduction to obtain a signal group after adding white noise.

[0146] For example, random white noise {n1(t), n2(t), ..., n v (t)}, then the initial signal data after adding random noise becomes a set of signal groups {x1(t),x2(t),…,x v (t)}, the relationship between the signal containing random white noise and the initial signal data after denoising without white noise is as follows:

[0147]

[0148] Among them, n v is the vth random white noise, x v is the initial signal data after denoising after adding white noise for the vth time, and v is the number of components of random white noise added.

[0149] S208: Performing empirical mode decomposition on each signal in the signal group after adding white noise, and extracting several groups of intrinsic mode function components and residuals.

[0150] For example, after adding white noise to the signal group {x1(t),x2(t),…,x v (t)} for each signal x i (t)(i=1,2,…,v) are decomposed by EMD respectively to obtain v groups of intrinsic mode function components {[c 11 ,c 12 ,…,c 1w ],[c 21 ,c 22 ,…,c 2w ],…[c v1 ,c v2 ,…,c vm ]} (denoted as C) and v group residual [r 1w ,r 2w ,…,r vw ] T Denoted as (R).

[0151] S209: Determine an intrinsic mode function component matrix and a residual matrix according to a plurality of groups of intrinsic mode function components and residuals.

[0152] Exemplarily, the intrinsic mode function component matrix is ​​shown as follows:

[0153]

[0154] Wherein, v is the number of rows of the intrinsic mode function component matrix, that is, the number of components of the added random white noise, and w is the number of columns of the intrinsic mode function component matrix, that is, the number of intrinsic mode function components.

[0155] The residual matrix is ​​shown as follows:

[0156]

[0157] Where v is the number of rows in the residual matrix, which is the same as the number of rows in the intrinsic mode function component matrix, and w is the number of columns in the residual matrix, which is the same as the number of columns in the intrinsic mode function component matrix.

[0158] S210: Calculating the intrinsic mode function component mean and the residual mean for the intrinsic mode function component matrix and the residual matrix column by column respectively.

[0159] Exemplarily, the eigenmode function component means and the residual means are calculated column by column for the eigenmode function component matrix and the residual matrix, and the reconstructed data is obtained according to the eigenmode function component means and the residual means. Specifically, the matrix C is calculated column by column to obtain the means corresponding to each column of the v groups of eigenmode function components [C1, C2, ..., C w ] is shown in the following formula:

[0160]

[0161] The mean R of the m groups of residuals is as follows:

[0162]

[0163] S211: Obtain reconstructed data according to the intrinsic mode function component mean and the residual mean.

[0164] Exemplarily, the reconstructed data x'(t) is shown in the following formula:

[0165]

[0166] S212: Construct a back propagation neural network based on the reconstructed data, introduce the crossover probability and mutation probability in the genetic algorithm to correct the weights and thresholds of the back propagation neural network, and determine the hybrid network.

[0167] S213: Obtaining the fault diagnosis probability of each sensor according to the hybrid network.

[0168] S214: Determine the evidence reliability factor according to the inherent reliability coefficient of each sensor and the inherent reliability coefficient of the hybrid network.

[0169] S215: Correct the fault diagnosis probability of each sensor according to the evidence reliability factor, determine the evidence probability, and predict the motor fault based on the evidence probability.

[0170] It should be noted that steps S212-S15 are Figure 1 This has been discussed in detail in the implementation scenario shown and will not be repeated here.

[0171] From the above description, it can be known that the present invention adds enough different groups of random white noise to the original signal data for many times and then performs empirical mode decomposition to obtain a group of intrinsic mode function components, and then uses the characteristic that the mean of random white noise is zero to calculate the overall average value of each group of intrinsic mode function components obtained by all empirical mode decompositions, so as to eliminate the influence of the added random white noise. The addition of random white noise provides a relatively consistent reference scale for the empirical mode decomposition method to ensure the continuity of each modal function in the time domain, thereby reducing the problem of modal aliasing. And when multiple evidences are synthesized, the credibility coefficients between the various evidences are considered to improve the accuracy and credibility of the overall diagnosis.

[0172] like Figure 3 As shown, Figure 3 A flow chart of another embodiment of a motor fault diagnosis method based on multi-sensor data fusion provided by the present invention. A motor fault diagnosis method based on multi-sensor data fusion, the method comprising:

[0173] S301: Determine the number of nodes in the input layer, hidden layer, and output layer of the back propagation neural network according to the reconstructed data, and construct the back propagation neural network.

[0174] Exemplarily, the number of nodes in the input layer, hidden layer and output layer of the back-propagation neural network, as well as the algorithm used for training, are determined based on existing data to construct a complete back-propagation neural network.

[0175] S302: Set the population size and evolution times of the particle swarm, the initial position and initial velocity of each particle, the inertia weight and the acceleration factor. Each particle represents a set of weights and thresholds of the back propagation neural network.

[0176] Exemplarily, the population size and evolution times of the particle swarm, the initial position and initial velocity of each particle, the inertia weight, and the acceleration factor are set.

[0177] Specifically, the inertia weight is defined as follows:

[0178] ω(k)=ω start -(ω start -ω end )×k / T max ;

[0179] Among them, ω start is the initial inertia weight, ω end is the inertia weight of the maximum number of iterations, k is the current number of iterations, T max is the maximum number of iterations.

[0180] Define the particle's acceleration factors c1 and c2, c1=c2=2.05.

[0181] S303: Update the initial velocity and initial position of each particle according to the inertia weight and the acceleration factor until a preset population size is reached, and obtain the optimal velocity and optimal position of each particle.

[0182] S304: Changing the optimal position of the particle according to the crossover probability and mutation probability in the genetic algorithm.

[0183] S305: According to the optimal position of the changed particle, the individual optimal fitness and the population optimal fitness of the particle swarm are determined to obtain the optimal particle swarm.

[0184] S306: Modify the weights and thresholds of the back propagation neural network according to the optimal particle swarm until a preset number of evolutions is reached to determine the hybrid network.

[0185] Exemplarily, an improved particle swarm optimization algorithm that introduces crossover probability and mutation probability in a genetic algorithm is combined with a back propagation neural network to obtain a hybrid network, which is used to perform training and judgment of motor fault diagnosis.

[0186] Verified by the data set, the results obtained by the improved hybrid network prediction are relatively stable, with the mean square error stable at around 0.04, the average value being 0.0414, and the running time varying around 0.5S, with an average value of 0.5351s. The mean square error of the original back propagation neural network is more random, with an average value of 0.1004, and the running time is mostly greater than 1s, with an average value of 1.4637s. In comparison, the improved PSO_BP network has better reliability.

[0187] From the above description, it can be seen that in the hybrid network, the purpose of using the particle swarm optimization algorithm to optimize the back propagation neural network is to better correct the weights and thresholds of the back propagation neural network, optimize the network structure, prevent the back propagation neural network from falling into the local minimum point, and improve the overall operation efficiency of the hybrid network.

[0188] like Figure 4 As shown, Figure 4 A schematic diagram of a flow chart of an embodiment of a motor fault diagnosis system based on multi-sensor data fusion provided by the present invention. A motor fault diagnosis system 10 based on multi-sensor data fusion, the system comprising:

[0189] The initial signal data acquisition module 11 is used to acquire the initial signal data of each sensor.

[0190] The signal denoising module 12 is used to perform denoising processing on the initial signal data by singular value decomposition.

[0191] The reconstructed data acquisition module 13 is used to extract the intrinsic mode function components of the initial signal data and acquire the reconstructed data according to the intrinsic mode function components.

[0192] The hybrid network determination module 14 is used to construct a back propagation neural network based on the reconstructed data, introduce the crossover probability and mutation probability in the genetic algorithm to correct the weights and thresholds of the back propagation neural network, and determine the hybrid network.

[0193] The module 15 for acquiring the fault diagnosis probability of each sensor is used to acquire the fault diagnosis probability of each sensor according to the hybrid network.

[0194] The evidence reliability factor determination module 16 is used to determine the evidence reliability factor according to the inherent reliability coefficient of each sensor and the inherent reliability coefficient of the hybrid network.

[0195] The motor fault prediction module 17 is used to correct the fault diagnosis probability of each sensor according to the evidence reliability factor, determine the evidence probability, and predict the motor fault according to the evidence probability.

[0196] Exemplarily, in the initial signal data acquisition module 11, the initial signal data of each sensor is acquired. In the signal denoising module 12, the Hankel matrix of the initial signal data is determined by phase space reconstruction, and the number of rows and columns of the Hankel matrix is ​​determined according to the number of signal samples of the initial signal data; singular value decomposition is performed on the Hankel matrix to obtain singular values, and the degree of change between adjacent singular values ​​is determined; it is determined that the degree of change between the current singular value and the next singular value has a sudden change, and the singular value is the dividing point; all singular values ​​before the dividing point are retained, and the remaining singular values ​​are set to zero, and a new Hankel matrix is ​​obtained according to the inverse process of singular value decomposition; according to the inverse process of phase space reconstruction, the new Hankel matrix is ​​converted into the initial signal data after denoising. Furthermore, in the reconstructed data acquisition module 13, random white noise with zero mean and equal variance is added to the initial signal data after denoising to obtain the signal group after adding white noise; empirical mode decomposition is performed on each signal in the signal group after adding white noise to extract several groups of intrinsic mode function components and residuals; an intrinsic mode function component matrix and a residual matrix are determined according to the several groups of intrinsic mode function components and residuals; the intrinsic mode function component mean and the residual mean are calculated column by column for the intrinsic mode function component matrix and the residual matrix respectively; and the reconstructed data is obtained according to the intrinsic mode function component mean and the residual mean. Further, in the hybrid network determination module 14, the number of nodes in the input layer, hidden layer and output layer of the back propagation neural network is determined according to the reconstructed data, and the back propagation neural network is constructed; the population size and evolution times of the particle swarm, the initial position and initial velocity of each particle, the inertia weight and the acceleration factor are set, and each particle represents a set of weights and thresholds of the back propagation neural network; according to the inertia weight and the acceleration factor, the initial velocity and initial position of each particle are updated until the preset population size is reached, and the optimal velocity and optimal position of each particle are obtained; the optimal position of the particle is changed according to the crossover probability and the mutation probability; according to the optimal position of the changed particle, the individual optimal fitness and the population optimal fitness of the particle swarm are determined to obtain the optimal particle swarm; according to the optimal particle swarm, the weights and thresholds of the back propagation neural network are corrected until the preset evolution times are reached, and the hybrid network is determined. Further, in the fault diagnosis probability acquisition module 15 of each sensor, the fault diagnosis probability of each sensor is obtained according to the hybrid network. Finally, in the evidence reliability factor determination module 16, the evidence reliability factor is determined according to the inherent reliability coefficient of each sensor and the inherent reliability coefficient of the hybrid network.

[0197] like Figure 5 As shown, Figure 5 The structure diagram of an embodiment of the medium provided by the present invention is shown in FIG. The medium 20 stores at least one computer program 21, and the computer program 21 is executed by a processor 22 to implement the following Figure 1 , Figure 2 ,and Figure 3In one embodiment, the storage medium 20 may be a storage chip, a hard disk, a mobile hard disk, a USB flash drive, an optical disk, or other readable and writable storage tools, or a server, etc.

[0198] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0199] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer-readable storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0200] The apparatus, device, non-volatile computer-readable storage medium and method provided in the embodiments of this specification correspond to each other, and therefore, the apparatus, device, and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the corresponding apparatus, device, and non-volatile computer storage medium will not be repeated here.

[0201] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0202] For the convenience of description, the above device is described by being divided into various units according to their functions and described separately. Of course, when implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware. It should be understood by those skilled in the art that this specification embodiment can be provided as a method, system, or computer program product. Therefore, this specification embodiment can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification embodiment can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0203] This specification is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0204] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0205] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0206] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0207] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0208] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0209] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0210] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0211] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0212] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A motor fault diagnosis method based on multi-sensor data fusion, characterized in that: The method comprises: Obtaining initial signal data of each sensor; The initial signal data is subjected to noise reduction processing by singular value decomposition; Extracting the intrinsic mode function components of the initial signal data after noise reduction, and obtaining reconstructed data according to the intrinsic mode function components; Constructing a back propagation neural network based on the reconstructed data, introducing the crossover probability and mutation probability in the genetic algorithm to modify the weights and thresholds of the back propagation neural network, and determining a hybrid network; Obtaining the fault diagnosis probability of each sensor according to the hybrid network; Determining an evidence reliability factor according to the inherent reliability coefficients of the sensors and the inherent reliability coefficient of the hybrid network; The fault diagnosis probability of each sensor is corrected according to the evidence reliability factor, the evidence probability is determined, and the motor fault prediction is performed based on the evidence probability.

2. A motor fault diagnosis method based on multi-sensor data fusion according to claim 1, characterized in that: The denoising process of the initial signal data by singular value decomposition specifically includes: Determine the Hankel matrix of the initial signal data by phase space reconstruction, and determine the number of rows and columns of the Hankel matrix according to the number of signal samples of the initial signal data; Performing singular value decomposition on the Hankel matrix to obtain singular values, and determining the degree of change between adjacent singular values; Determine that the degree of change between the current singular value and the next singular value has a sudden change, then the singular value is a demarcation point; retaining all singular values ​​before the demarcation point, setting the remaining singular values ​​to zero, and obtaining a new Hankel matrix according to the inverse process of the singular value decomposition; According to the inverse process of the phase space reconstruction, the new Hankel matrix is ​​converted into the initial signal data after noise reduction.

3. A motor fault diagnosis method based on multi-sensor data fusion according to claim 2, characterized in that: Determining the Hankel matrix of the initial signal by phase space reconstruction, and determining the number of rows and columns of the Hankel matrix according to the number of signal samples of the initial signal, specifically includes: pass Determine the Hankel matrix of the initial signal data, where x1, x2, ..., x N is the initial signal data, m is the number of rows of the Hankel matrix, n is the number of columns of the Hankel matrix, D m×n is the signal subspace without noise interference, W m×n is the noise signal subspace; according to Determine the number of rows of the Hankel matrix, where N is the number of signal samples; The number of columns of the Hankel matrix is ​​determined according to n=N+1-m.

4. The motor fault diagnosis method based on multi-sensor data fusion according to claim 3 is characterized in that: The step of performing singular value decomposition on the Hankel matrix, obtaining singular values, and determining the degree of change between each singular value and the next singular value specifically includes: according to Performing singular value decomposition on the Hankel matrix, wherein m is the number of rows of the Hankel matrix, n is the number of columns of the Hankel matrix, U and V are both orthogonal matrices, and Σ is a diagonal matrix; according to Get the singular values, where S = diag(σ1,σ2,…,σ r ), r is the rank of the Hankel matrix, r∈[1,m], σ i is the i-th singular value in the Hankel matrix, and σ i ≥σ i+1 , i∈[1,r-1]; according to Determine how much each singular value varies from the next, where a i is the i-th singular value, a max is the maximum value among the singular values, a min is the minimum value among the singular values.

5. The motor fault diagnosis method based on multi-sensor data fusion according to claim 2 is characterized in that: The extracting the intrinsic mode function component of the initial signal data after noise reduction and obtaining the reconstructed data according to the intrinsic mode function component specifically includes: Adding random white noise with a mean of zero and an equal variance to the initial signal data after noise reduction to obtain a signal group after adding white noise; Performing empirical mode decomposition on each signal in the signal group after adding white noise, and extracting several groups of intrinsic mode function components and residuals; Determining an intrinsic mode function component matrix and a residual matrix according to the plurality of groups of intrinsic mode function components and residuals; Calculating the intrinsic mode function component mean and the residual mean by column for the intrinsic mode function component matrix and the residual matrix respectively; Reconstructed data is obtained according to the intrinsic mode function component mean and the residual mean.

6. The motor fault diagnosis method based on multi-sensor data fusion according to claim 5 is characterized in that: The step of obtaining the reconstructed data according to the intrinsic mode function component mean and the residual mean specifically includes: according to Get the reconstructed data, where C i is the mean value corresponding to each column in the intrinsic mode function component matrix, i∈[1,w], w is the number of intrinsic mode function components, v is the number of components of random white noise added, R is the residual mean, r iw is the residual of the i-th column in the residual matrix.

7. The motor fault diagnosis method based on multi-sensor data fusion according to claim 5 is characterized in that: The back propagation neural network is constructed based on the reconstructed data, and the weights and thresholds of the back propagation neural network are corrected by the crossover probability and the mutation probability to determine the hybrid network, which specifically includes: Determine the number of nodes in the input layer, hidden layer and output layer of the back propagation neural network according to the reconstructed data, and construct the back propagation neural network; Set the population size and evolution times of the particle swarm, the initial position and initial velocity of each particle, the inertia weight and the acceleration factor. Each particle represents a set of weights and thresholds of the back propagation neural network. According to the inertia weight and the acceleration factor, the initial speed and initial position of each particle are updated until a preset population size is reached, and the optimal speed and optimal position of each particle are obtained; Change the optimal position of particles according to the crossover probability and mutation probability; According to the optimal position of the changed particle, determine the individual optimal fitness and the population optimal fitness of the particle group to obtain the optimal particle group; The weights and thresholds of the back propagation neural network are modified according to the optimal particle swarm until a preset number of evolutions is reached to determine a hybrid network.

8. The motor fault diagnosis method based on multi-sensor data fusion according to claim 7 is characterized in that: The method of correcting the fault diagnosis probability of each sensor according to the evidence reliability factor, determining the evidence probability, and predicting the motor fault according to the evidence probability specifically includes: according to Determine the probability of evidence, where m ij ′=β i m ij , m ij is the fault diagnosis probability of each sensor in the jth case of the i-th evidence, i∈[1,L′], j∈[1,N], L′ is the number of independent evidences, N′ is the number of evaluation levels, m ij ′ is the optimized fault diagnosis probability of each sensor, and Θ is the completely uncertain situation in the fault situation.

9. A motor fault diagnosis system based on multi-sensor data fusion, characterized in that: The system comprises: An initial signal data acquisition module is used to acquire initial signal data of each sensor; A signal denoising module, used for denoising the initial signal data by singular value decomposition; A reconstruction data acquisition module, used for extracting the intrinsic mode function components of the initial signal data, and acquiring the reconstruction data according to the intrinsic mode function components; A hybrid network determination module is used to construct a back propagation neural network based on the reconstructed data, introduce the crossover probability and mutation probability in the genetic algorithm to correct the weights and thresholds of the back propagation neural network, and determine the hybrid network; A module for acquiring the fault diagnosis probability of each sensor, used for acquiring the fault diagnosis probability of each sensor according to the hybrid network; An evidence reliability factor determination module, used to determine the evidence reliability factor according to the inherent reliability coefficients of the sensors and the inherent reliability coefficient of the hybrid network; The motor fault prediction module is used to correct the fault diagnosis probability of each sensor according to the evidence reliability factor, determine the evidence probability, and predict the motor fault based on the evidence probability.

10. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 8.

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