Motor stator winding degradation feature extraction method and system based on multi-dimensional multi-scale associated sample entropy

Abstract

The invention discloses a motor stator winding degradation feature extraction method and system based on multi-dimensional multi-scale correlation sample entropy, and the method comprises the steps: collecting a multi-dimensional original time signal matrix of a motor stator winding according to the operation parameters of a motor; performing coarse-grained reconstruction on the multi-dimensional original time signal matrix to obtain a one-dimensional signal matrix after coarse-grained reconstruction; reconstructing the one-dimensional signal matrix to obtain a reconstructed signal matrix; constructing a nested matrix according to the signal matrix, and calculating the number of matrixes meeting conditions according to a set similarity threshold; and calculating a multi-correlation multi-scale sample entropy according to the number of the matrixes, and determining a real-time reliability index of the motor stator winding according to the multi-correlation multi-scale sample entropy so as to evaluate the degradation state of the motor stator winding. According to the invention, the cross correlation information of the signal can be captured among different dimensions, the complexity of the signal is accurately evaluated, the operation condition of the motor winding is detected in real time, and the motor winding fault and further deterioration after the fault are avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of motor reliability analysis, and particularly relates to a method and system for extracting degradation characteristics of a motor stator winding based on multi-dimensional multi-scale correlation sample entropy. Background Art

[0002] As a complex system, the performance of a motor will gradually degrade from a healthy state to a failure state, and this process is continuous and gradual. In actual operation, the degradation speed of the motor may accelerate due to the occurrence of faults. To effectively prevent the occurrence of serious faults or even failures, it is crucial to timely observe the degradation degree of the motor system and detect early fault signs. Therefore, it is very necessary to propose a parameter that can effectively characterize the degradation degree of the motor. Traditional evaluation methods are usually based on experience and evaluate the performance state of the motor through manual observation and empirical judgment.

[0003] However, these traditional methods have obvious deficiencies. First, the experience-based evaluation methods are subjective, and the experience and judgment criteria of different operators may vary, resulting in difficulty in ensuring the consistency of evaluation results. Second, these methods are difficult to quantify the degradation degree of motor performance and cannot provide accurate evaluation data. In addition, traditional methods lack real-time performance and are difficult to capture the rapid degradation of motor performance. A motor is a complex system affected by various factors such as load changes and environmental conditions, and traditional methods often cannot effectively consider and analyze these complexities, thus unable to make accurate evaluations. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes a method and system for extracting degradation characteristics of a motor stator winding based on multi-dimensional multi-scale correlation sample entropy to solve the problems existing in the above prior art.

[0005] To achieve the above object, in a first aspect, the present invention provides a method for extracting degradation characteristics of a motor stator winding based on multi-dimensional multi-scale correlation sample entropy, including:

[0006] Collect a multi-dimensional original time signal matrix of the motor stator winding according to the operating parameters of the motor;

[0007] Perform coarse-grained reconstruction on the multi-dimensional original time signal matrix to obtain a one-dimensional signal matrix after coarse-grained reconstruction;

[0008] Reconstruct the one-dimensional signal matrix to obtain a reconstructed signal matrix;

[0009] Construct a nested matrix according to the signal matrix, and calculate the number of matrices that meet the conditions according to a set similarity threshold;

[0010] Calculate the multi - correlated multi - scale sample entropy according to the number of matrices, and determine the real - time reliability index of the motor stator winding based on the multi - correlated multi - scale sample entropy to evaluate the degradation state of the motor stator winding.

[0011] Preferably, collecting the multi - dimensional original time - signal matrix of the motor stator winding includes:

[0012] Set the operating parameters of speed and torque that characterize the motor performance;

[0013] Use the sampling period to sample the current of each phase winding of the motor to obtain vectors, and form a multi - dimensional original time - signal matrix.

[0014] Preferably, the multi - dimensional original time - signal matrix is:

[0015] X h =[x h (1),x h (2),…,x h (N)],

[0016] where N represents the number of sampling points of the current of each phase winding, h represents the h - th phase current; u is the signal length, f is the frequency of the original signal, and T is the sampling interval.

[0017] Preferably, the coarse - graining reconstruction of the collected multi - dimensional original time - signal matrix includes:

[0018] Select different scales, and perform the first - stage coarse - graining reconstruction on the multi - dimensional original time - signal matrix according to the coarse - graining reconstruction equation to obtain the reconstructed one - dimensional signal matrix.

[0019] Preferably, the coarse - graining reconstruction equation is:

[0020] where, h represents the h - th phase current; x h (i) and x h (i + u) are the i - th and i + u - th points of the original signal X h , is the j - th point of the vector Y h at scale t.

[0021] Preferably, calculating the number of matrices that meet the conditions includes:

[0022] Construct a nested matrix according to the signal matrix;

[0023] Calculate the number of matrices whose Euclidean distance between matrices is less than the similarity threshold according to the nested matrix.

[0024] Preferably, the signal matrix is:

[0025]

[0026] Among them, Z h is composed of [(N - u) / t] - m + 1 vectors of length m, and the elements Z h (w) is the w-th column vector in the vector group Z h where m is the length of the column vector;

[0027] The nested matrix is as follows:

[0028]

[0029] Preferably, determining the real-time reliability index of the motor stator winding includes:

[0030] Calculating the real-time reliability index according to the sum of the MCMSE of the three-phase stator winding currents of the permanent magnet synchronous motor in the degraded state and the healthy state under multiple selected scales t.

[0031] In a second aspect, the present invention also discloses a system for extracting degradation characteristics of a motor stator winding based on multi-dimensional multi-scale correlation sample entropy, including:

[0032] A signal acquisition module, configured to acquire a multi-dimensional original time signal matrix of the motor stator winding according to the operating parameters of the motor;

[0033] A first reconstruction module, configured to perform coarse-grained reconstruction on the multi-dimensional original time signal matrix to obtain a one-dimensional signal matrix after coarse-grained reconstruction;

[0034] A second reconstruction module, configured to reconstruct the one-dimensional signal matrix to obtain a reconstructed signal matrix;

[0035] A first calculation module, configured to construct a nested matrix according to the signal matrix, and calculate the number of matrices that meet the conditions according to a set similarity threshold;

[0036] A second calculation module, configured to calculate the multi-correlation multi-scale sample entropy according to the number of matrices, and determine the real-time reliability index of the motor stator winding according to the multi-correlation multi-scale sample entropy to evaluate the degradation state of the motor stator winding.

[0037] In a third aspect, the present invention also discloses a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0038] Compared with the prior art, the present invention has the following advantages and technical effects:

[0039] The present invention discloses a method for extracting the degradation characteristics of a motor stator winding based on multi-dimensional multi-scale correlation sample entropy. First, according to the operating parameters of the motor, a multi-dimensional original time signal matrix of the motor stator winding is collected; secondly, the multi-dimensional original time signal matrix is coarsely grained and reconstructed to obtain a one-dimensional signal matrix after coarse-grained reconstruction; then, the one-dimensional signal matrix is reconstructed to obtain a reconstructed signal matrix; further, according to the signal matrix, a nested matrix is constructed, and according to a set similarity threshold, the number of matrices meeting the conditions is calculated; finally, according to the number of matrices, the multi-correlation multi-scale sample entropy is calculated, and according to the multi-correlation multi-scale sample entropy, the real-time reliability index of the motor stator winding is determined to evaluate the degradation state of the motor stator winding.

[0040] The present invention improves the multi-scale sample entropy so that it can process multi-dimensional periodic signals and can capture the cross-correlation information of signals between different dimensions, thereby more accurately evaluating the complexity of the signals and applying it to the field of motor winding degradation evaluation and reliability analysis; the present invention can accurately and real-time detect the operating condition of the motor winding, avoid the motor winding failure and further deterioration after the failure, and has good engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings constituting a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0042] Figure 1 is a flowchart of the method of the embodiment of the present invention;

[0043] Figure 2 is a comparison schematic diagram of the multi-dimensional multi-scale correlation sample entropy of the motor winding under different degradation conditions of the embodiment of the present invention;

[0044] Figure 3 is a comparison schematic diagram of the real-time reliability index of the motor winding under different degradation conditions of the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the drawings and combine the embodiments to detail this application.

[0046] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0047] Embodiment 1

[0048] As shown Figure 1-2 in the figure, this embodiment takes a three-phase permanent magnet motor as an example, and provides a method for extracting the degradation characteristics of the motor stator winding based on multi-dimensional multi-scale correlation sample entropy, and compares the results of the motor in three different states. The method includes:

[0049] S1. According to the operating parameters of the motor, collect the multi-dimensional original time signal matrix of the motor stator winding;

[0050] Further, set the rotational speed and torque operating parameters representing the motor performance according to the actual parameters of the motor, collect the three-phase current of the motor through a current sensor, sample the three-phase stator current using the sampling interval T, and obtain the vector X h , form the multi-dimensional original time signal matrix X, and determine the signal frequency length u;

[0051] Specifically X h =[x h (1), x h (2), …, x h (N)],

[0052] where N represents the number of sampling points of each phase winding current, h represents the h-phase current, 1 ≤ h ≤ 3; u is the signal frequency length, f is the frequency of the original signal, and T is the sampling interval.

[0053] In this embodiment, the rotational speed of the permanent magnet synchronous motor is set to 500 r / min, and the load torque is 3 N·m; the fixed sampling interval T = 0.0001 s is set, the three-phase current frequency f = 50 Hz is calculated, u = 200, N = 1000 is obtained, and the multi-dimensional original time signal matrix X has a total of 1000 × 3 elements.

[0054] In this embodiment, five motor degradation states are set to verify the effectiveness of this method. The five motor winding states are shown in Table 1.

[0055] Table 1

[0056] Health The motor is in a healthy state State 1 Inter-turn short circuit: 10% State 2 Inter-turn short circuit: 20% State 3 Inter-turn short circuit: 30% State 4 Inter-turn short circuit: 40% State 5 Inter-turn short circuit: 50%

[0057] S2. Coarsely granulate and reconstruct the multi-dimensional original time signal matrix to obtain the one-dimensional signal matrix after coarse granulation and reconstruction;

[0058] Further, select different scales t to perform the first coarse granulation and reconstruction on the original signal matrix X to obtain the reconstructed signal matrix;

[0059] In this embodiment, the original signal matrix is coarsely granulated and reconstructed using the coarse granulation reconstruction equation to obtain the matrix

[0060] The coarse-grained reconstruction equation is as follows:

[0061] Wherein, h represents the h-phase current, where 1 ≤ h ≤ 3; x h (i) and x h (i + u) are the i-th and (i + u)-th points of the original signal X h , is the j-th point of the vector Y h at scale t, and j satisfies:

[0062] Let the scale be t. Specifically, let 1 ≤ t ≤ 5, and calculate respectively when t takes different values;

[0063] For example, when t = 1, the signal matrix after coarse-grained reconstruction is:

[0064]

[0065] S3. Reconstruct the one-dimensional signal matrix to obtain the reconstructed signal matrix;

[0066] In this embodiment, the one-dimensional signal vector Y h after coarse-grained reconstruction is reconstructed to obtain the signal matrix Z h ;

[0067] Z h is composed of [(N - u) / t] - m + 1 vectors of length m, and the elements Z h (w) is the w-th column vector in the vector group Z h , and m is the length of the column vector.

[0068] Specifically, in this embodiment

[0069] S4. According to the signal matrix, construct a nested matrix, and calculate the number of matrices that meet the conditions according to the set similarity threshold;

[0070] Furthermore, construct a nested matrix Determine the similarity r, and determine the number s of matrices in the matrix Z'(w) = [Z 1 (w), Z 2 (w), Z 3 (w)] that satisfy d[Z'(i), Z'(j)] = max(Euc(Z'(i) - Z'(j))) ≤ r m .

[0071] Among them, the meaning of max[Euc(Z'(p)-Z'(q))] is the maximum Euclidean distance of all corresponding vectors between matrix Z'(p) and matrix Z'(q), and p≠q; r represents the similarity, 0.1·SD(x) ≤ r ≤ 0.25·SD(x), and SD(x) is the standard deviation between the row elements of the original data matrix X.

[0072] Specifically, in this embodiment:

[0073]

[0074] Set the value of r as: r = 0.15·SD(x) ≈ 0.083, and SD(x) represents the standard deviation between the row elements of the original data matrix X.

[0075] S5. Calculate the multi - correlation multi - scale sample entropy according to the number of matrices, and determine the real - time reliability index of the motor stator winding according to the multi - correlation multi - scale sample entropy to evaluate the degradation state of the motor stator winding.

[0076] Further, take m' = m + 1, calculate to obtain s m’ and calculate the real - time reliability index k of the motor stator winding w to judge the real - time performance degradation state and reliability of the motor.

[0077] In this embodiment, set m' = m + 1 to construct a nested matrix Calculate d[Z'(p') - Z'(q')] = max[Euc(Z'(p') - Z'(q'))] ≤ r, determine the number of matrices that satisfy this formula, denoted as s m’ . p’≠q’.

[0078] Calculate the real - time reliability index of the motor stator winding to judge the real - time performance degradation state and reliability of the motor. MCMSE d (t) is the sum of MCMSE at different scales (scale from 1 to t) under the degraded state of the permanent - magnet synchronous motor; MCMSE h (t) is the sum of MCMSE at different scales (scale from 1 to t) under the healthy state of the permanent - magnet synchronous motor, The reliability decreases as the value of k w rises.

[0079] Specifically, in this embodiment, take m' = 3, calculate to obtain s 3 ,

[0080] According to the formula realize the reliable index detection of the degradation state of the motor stator winding.

[0081] For the six states including the five degradation states and the healthy state shown in Table 1, the real-time reliability indices for each scale are calculated, and the results are as Figure 3 shown. It can be clearly seen that the real-time reliability indices of the winding gradually increase at each scale from degradation state 1 to degradation state 5, which proves that the stator winding of the motor has degraded. Moreover, since the degradation degree from state 1 to state 5 increases in sequence, the reliability indices k w of state 1 to state 5 increase in sequence, which is in line with the theoretical prediction.

[0082] For example, for a three-phase permanent magnet synchronous motor, first set the operating parameters of the motor; then collect the operation and maintenance parameters of the three-phase stator current of the motor in the healthy state and the real-time state at the same sampling interval; finally, calculate its real-time reliability index based on the improved multi-correlation multi-scale sample entropy, so as to judge the real-time performance degradation state of the motor winding and the reliability of the motor winding.

[0083] A method for extracting the degradation characteristics of the motor stator winding based on multi-dimensional multi-scale correlation sample entropy proposed by the present invention is applicable to other motor systems with different topological structures.

[0084] Embodiment 2

[0085] Based on the same inventive concept, this embodiment also provides a system for extracting the degradation characteristics of the motor stator winding based on multi-dimensional multi-scale correlation sample entropy, including:

[0086] A signal acquisition module, configured to collect a multi-dimensional original time signal matrix of the motor stator winding according to the operating parameters of the motor;

[0087] A first reconstruction module, configured to perform coarse-grained reconstruction on the multi-dimensional original time signal matrix to obtain a one-dimensional signal matrix after coarse-grained reconstruction;

[0088] A second reconstruction module, configured to reconstruct the one-dimensional signal matrix to obtain a reconstructed signal matrix;

[0089] A first calculation module, configured to construct a nested matrix according to the signal matrix, and calculate the number of matrices that meet the conditions according to a set similarity threshold;

[0090] A second calculation module, configured to calculate the multi-correlation multi-scale sample entropy according to the number of matrices, and determine the real-time reliability index of the motor stator winding according to the multi-correlation multi-scale sample entropy to evaluate the degradation state of the motor stator winding.

[0091] A system for extracting the degradation characteristics of the motor stator winding based on multi-dimensional multi-scale correlation sample entropy provided in this embodiment has all the advantages of the method for extracting the degradation characteristics of the motor stator winding based on multi-dimensional multi-scale correlation sample entropy provided in Embodiment 1.

[0092] Embodiment III

[0093] This embodiment also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in Embodiment I are implemented.

[0094] The above are only the preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A motor stator winding degradation feature extraction method based on multi-dimensional and multi-scale correlation sample entropy, characterized in that: The following steps are involved: According to the operating parameters of the motor, a multi-dimensional original time signal matrix of the motor stator winding is collected; Performing coarse-grained reconstruction on the multi-dimensional original time signal matrix to obtain a coarse-grained reconstructed one-dimensional signal matrix; Reconstructing the one-dimensional signal matrix to obtain a reconstructed signal matrix; According to the signal matrix, a nested matrix is ​​constructed, and according to a set similarity threshold, the number of matrices that meet the conditions is calculated; According to the number of matrices, multi-correlation multi-scale sample entropy is calculated, and the real-time reliability index of the motor stator winding is determined according to the multi-correlation multi-scale sample entropy to evaluate the degradation state of the motor stator winding.

2. The method according to claim 1, characterized in that: The multi-dimensional original time signal matrix of the motor stator winding is collected, including: Set the speed and torque operating parameters that characterize the motor performance; The current of each phase winding of the motor is sampled using a sampling period to obtain a vector, which constitutes a multi-dimensional original time signal matrix.

3. The method according to claim 1, characterized in that: The multi-dimensional original time signal matrix is: X h =[x h (1),x h (2),…,x h (N)], Wherein, N represents the number of sampling points of each phase winding current, h represents the hth phase current; u is the signal frequency, f is the frequency of the original signal, and T is the sampling interval.

4. The method according to claim 1, characterized in that: The coarse-grained reconstruction of the collected multi-dimensional original time signal matrix includes: Different scales are selected and the multi-dimensional original time signal matrix is ​​reconstructed for the first time according to the coarse-grained reconstruction equation to obtain the reconstructed one-dimensional signal matrix.

5. The method according to claim 1, characterized in that The coarse-grained reconstruction equation is: in, h represents the hth phase current; x h (i) and x h (i+u) is the original signal X h The i-th and i+u-th points of is the vector Y with scale t h The jth point of .

6. The method according to claim 1, characterized in that Calculating the number of matrices that meet the conditions includes: constructing a nested matrix according to the signal matrix; According to the nested matrix, the number of matrices whose Euclidean distances between matrices are less than a similarity threshold is calculated.

7. The method according to claim 1, characterized in that The signal matrix is: Among them, Z h It consists of [(Nu) / t]-m+1 vectors of length m, whose elements Z h (w) is the vector group Z h The wth column vector in , m is the length of the column vector; The nested matrix is:

8. The method according to claim 1, characterized in that Determine the real-time reliability index of the motor stator winding including: The real-time reliability index is calculated according to the sum of the MCMSE of the three-phase stator winding current of the permanent magnet synchronous motor in the degraded state and the healthy state under the selected multiple scales t.

9. A motor stator winding degradation feature extraction system based on multi-dimensional and multi-scale correlation sample entropy, characterized in that: include: A signal acquisition module is used to collect a multi-dimensional original time signal matrix of the motor stator winding according to the motor's operating parameters; A first reconstruction module is used to perform coarse-grained reconstruction on the multi-dimensional original time signal matrix to obtain a one-dimensional signal matrix after coarse-grained reconstruction; A second reconstruction module, used for reconstructing the one-dimensional signal matrix to obtain a reconstructed signal matrix; A first calculation module, used to construct a nested matrix according to the signal matrix, and calculate the number of matrices that meet the conditions according to a set similarity threshold; The second calculation module is used to calculate the multi-correlation multi-scale sample entropy according to the number of matrices, and determine the real-time reliability index of the motor stator winding according to the multi-correlation multi-scale sample entropy to evaluate the degradation state of the motor stator winding.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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