Motor fault diagnosis method, intelligent equipment and storage medium

Abstract

The invention relates to the technical field of motors, in particular to a motor fault diagnosis method, intelligent equipment and a storage medium, and aims to solve the problem of how to accurately obtain a negative sequence component of a voltage / current fundamental frequency signal based on fast Fourier transform and spectrum leakage. And the technical problem of how to obtain the statistical mean value of the negative sequence impedance based on convolution to perform motor fault diagnosis is solved. On the basis of a three-phase voltage / current fundamental frequency signal sequence, an a-phase negative sequence component of a voltage / current fundamental frequency signal is obtained through fast Fourier transform according to frequency spectrum leakage, the fundamental frequency fluctuation characteristic of a power grid voltage / current signal can be effectively reserved, the extraction precision of the negative sequence component of the voltage / current fundamental frequency signal is improved, and the extraction accuracy of the voltage / current fundamental frequency signal is improved. Therefore, the impedance calculation is more accurate; the statistical mean value of the negative sequence impedance within the preset time length is calculated through convolution, the volatility of the negative sequence impedance calculation result is reduced, and the reliability of diagnosing the turn-to-turn short circuit fault of the motor based on the negative sequence impedance threshold value is improved.

Description

Technical Field

[0001] The present application relates to the technical field of motors, and particularly to a method for diagnosing motor faults, an intelligent device, and a storage medium. Background Art

[0002] As an asynchronous motor fault with an incidence rate second only to bearing faults, the inter-turn short-circuit fault seriously threatens the safe and stable operation of the asynchronous motor. Therefore, it is of great significance to perform real-time online diagnosis on the inter-turn short-circuit fault of the asynchronous motor. Currently, as a mainstream online diagnosis method for inter-turn short-circuit faults, the negative-sequence impedance analysis method mainly evaluates the unbalance degree of the three-phase electrical parameters of the asynchronous motor through the negative-sequence impedance of the asynchronous motor, and then diagnoses the inter-turn short-circuit fault.

[0003] In the application of diagnosing inter-turn short-circuit faults of asynchronous motors, although the negative-sequence impedance analysis method is simple and direct, due to the existence of many interference factors, the accuracy of its diagnostic results is not very ideal. In actual operation, since it is difficult to accurately extract the fundamental frequency signal of the stator line current, the negative-sequence impedance is mostly directly calculated using the voltage signal and the stator line current signal during diagnosis, which inevitably causes deviation in the calculation results. In addition, due to the volatility of the voltage signal, the impedance calculated by the phasor method also has relatively large volatility, which will also interfere with the threshold judgment. Therefore, how to improve the reliability of the negative-sequence impedance analysis method for diagnosing inter-turn short-circuit faults has become an urgent problem to be solved.

[0004] Correspondingly, a new motor fault diagnosis solution is needed in this field to solve the above problems. Summary of the Invention

[0005] In order to overcome the above defects, the present application is proposed to solve or at least partially solve the technical problem of how to accurately obtain the negative-sequence component of phase a of the voltage and current fundamental frequency signals with spectrum leakage based on the fast Fourier transform, and calculate the statistical mean of the negative-sequence impedance based on convolution, so as to more accurately diagnose the inter-turn short-circuit fault.

[0006] In a first aspect, a method for diagnosing motor faults is provided, and the method includes: Obtain a three-phase voltage signal sequence and a three-phase current signal sequence of the motor; Based on the three-phase voltage signal sequence, obtain a three-phase voltage fundamental frequency signal sequence, and based on the three-phase current signal sequence, obtain a three-phase current fundamental frequency signal sequence, where the time lengths of the three-phase voltage fundamental frequency signal sequence and the three-phase current fundamental frequency signal sequence are both a preset time length; Based on the three-phase voltage fundamental frequency signal sequence, obtain a negative-sequence component sequence of phase a of the voltage fundamental frequency signal, and based on the three-phase current fundamental frequency signal sequence, obtain a negative-sequence component sequence of phase a of the current fundamental frequency signal; Based on the negative sequence component sequence of phase a of the voltage fundamental frequency signal and the negative sequence component sequence of phase a of the current fundamental frequency signal, obtain the statistical mean of the negative sequence impedance within the preset duration through convolution; Based on the statistical mean of the negative sequence impedance and a preset negative sequence impedance threshold, determine whether the motor has a turn-to-turn short circuit fault.

[0007] In a technical solution of the above motor fault diagnosis method, the method further includes: Based on the three-phase voltage fundamental frequency signal sequence, through fast Fourier transform and considering spectral leakage, obtain the negative sequence component sequence of phase a of the voltage fundamental frequency signal; Based on the three-phase current fundamental frequency signal sequence, through fast Fourier transform and considering spectral leakage, obtain the negative sequence component sequence of phase a of the current fundamental frequency signal.

[0008] In a technical solution of the above motor fault diagnosis method, the method further includes: Perform fast Fourier transform on the three-phase fundamental frequency signal sequences respectively to obtain three-phase fundamental frequency domain signal sequences, where the three-phase fundamental frequency signal sequences include the three-phase voltage fundamental frequency signal sequences and the three-phase current fundamental frequency signal sequences, and the three-phase fundamental frequency domain signal sequences include three-phase voltage fundamental frequency domain signal sequences and three-phase current fundamental frequency domain signal sequences; Based on the three-phase voltage fundamental frequency domain signal sequence, obtain the frequency domain signal sequence of the negative sequence component of phase a of the voltage fundamental frequency signal through vector conversion, and perform inverse fast Fourier transform on the frequency domain signal sequence of the negative sequence component of phase a of the voltage fundamental frequency signal to obtain the negative sequence component sequence of phase a of the voltage fundamental frequency signal; Based on the three-phase current fundamental frequency domain signal sequence, obtain the frequency domain signal sequence of the negative sequence component of phase a of the current fundamental frequency signal through vector conversion, and perform inverse fast Fourier transform on the frequency domain signal sequence of the negative sequence component of phase a of the current fundamental frequency signal to obtain the negative sequence component sequence of phase a of the current fundamental frequency signal.

[0009] In a technical solution of the above motor fault diagnosis method, "Based on the three-phase voltage fundamental frequency domain signal sequence, obtain the frequency domain signal sequence of the negative sequence component of phase a of the voltage fundamental frequency signal through vector conversion" includes: Based on the fundamental frequency point number, symmetrically extract a preset number of sequence elements from the three-phase voltage fundamental frequency domain signal sequence to obtain a three-phase voltage fundamental frequency domain signal basis matrix, where the fundamental frequency point number is determined based on the fundamental frequency and the spectral resolution, and the preset number is determined based on the spectral leakage; Based on the three-phase voltage fundamental frequency domain signal basis matrix and the conversion vector, obtain the frequency domain basis vector of the negative sequence component of phase a of the voltage fundamental frequency signal; Expand the frequency-domain basis vector of the negative-sequence component of the a-phase of the voltage fundamental-frequency signal to obtain the frequency-domain signal sequence of the negative-sequence component of the a-phase of the voltage fundamental-frequency signal.

[0010] In a technical solution of the above motor fault diagnosis method, "based on the three-phase current fundamental-frequency signal sequence, obtain the frequency-domain signal sequence of the negative-sequence component of the a-phase of the current fundamental-frequency signal through the conversion vector" includes: Based on the fundamental-frequency point number, symmetrically extract a preset number of sequence elements from the three-phase current fundamental-frequency signal sequence to obtain the three-phase current fundamental-frequency signal basis matrix, where the fundamental-frequency point number is determined based on the fundamental frequency and the spectrum resolution, and the preset number is determined based on the spectrum leakage; Based on the three-phase current fundamental-frequency signal basis matrix and the conversion vector, obtain the frequency-domain basis vector of the negative-sequence component of the a-phase of the current fundamental-frequency signal; Expand the frequency-domain basis vector of the negative-sequence component of the a-phase of the current fundamental-frequency signal to obtain the frequency-domain signal sequence of the negative-sequence component of the a-phase of the current fundamental-frequency signal.

[0011] In a technical solution of the above motor fault diagnosis method, "based on the negative-sequence component sequence of the a-phase of the voltage fundamental-frequency signal and the negative-sequence component sequence of the a-phase of the current fundamental-frequency signal, obtain the statistical mean of the negative-sequence impedance within the preset duration through convolution" includes: Reconstruct and expand the negative-sequence component sequence of the a-phase of the voltage fundamental-frequency signal to obtain the voltage fundamental-frequency negative-sequence component sequence; Reconstruct the sequence number of the negative-sequence component sequence of the a-phase of the current fundamental-frequency signal to obtain the current fundamental-frequency negative-sequence component sequence; Based on the voltage fundamental-frequency negative-sequence component sequence, the current fundamental-frequency negative-sequence component sequence, and the parameters of the convolution kernel, obtain the statistical mean of the negative-sequence impedance, where the parameters of the convolution kernel are obtained according to the convolution equation between the sinusoidal voltage signal and the sinusoidal current signal.

[0012] In a technical solution of the above motor fault diagnosis method, the method further includes: Construct the convolution equation based on a convolution step size with a value of 2.

[0013] In a technical solution of the above motor fault diagnosis method, "judge whether the motor has an inter-turn short-circuit fault based on the statistical mean of the negative-sequence impedance and a preset negative-sequence impedance threshold" includes: Obtain the impedance change ratio between the statistical mean of the negative-sequence impedance and the negative-sequence impedance threshold; When the statistical mean of the negative-sequence impedance is less than the negative-sequence impedance threshold, determine the inter-turn short-circuit fault based on a preset impedance change interval.

[0014] In a second aspect, there is provided an intelligent device, which includes at least one processor; and a memory communicatively connected to the at least one processor; wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in any one of the technical solutions of the above-mentioned motor fault diagnosis method is implemented.

[0015] In a third aspect, a storage medium stores multiple program codes, and when the computer program is executed by the at least one processor, the method described in any one of the technical solutions of the above-mentioned motor fault diagnosis method is implemented.

[0016] One or more of the above technical solutions of the present application have at least one or more of the following beneficial effects: The method for obtaining the negative sequence component of the a-phase of the target voltage / current fundamental frequency signal based on Fourier transform and considering spectral leakage can effectively retain the fundamental frequency fluctuation characteristics of the grid voltage / current signal, improve the extraction accuracy of the negative sequence component of the voltage / current fundamental frequency signal, and thus make the calculation of impedance more accurate; By calculating the statistical mean value of the negative sequence impedance within a preset time length through convolution, the volatility of the negative sequence impedance calculation result is reduced, and the reliability of diagnosing the inter-turn short circuit fault of the motor based on the negative sequence impedance threshold is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Referring to the accompanying drawings, the disclosure of the present application will become easier to understand. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the protection scope of the present application.

[0018] Figure 1 is a schematic diagram of the main step flow of a motor fault diagnosis method according to an embodiment of the present application.

[0019] Figure 2 is a schematic diagram of the detailed step flow of step S103 according to an embodiment of the present application.

[0020] Figure 3 is a schematic diagram of the detailed step flow of step S202 according to an embodiment of the present application.

[0021] Figure 4 is an amplitude spectrum diagram obtained by performing a fast Fourier transform on the time domain signals of the a-phase voltage and the a-phase current according to an embodiment of the present application.

[0022] Figure 5 is a schematic diagram of the detailed step flow of step S204 according to an embodiment of the present application.

[0023] Figure 6It is a schematic diagram of the detailed step flow of step S104 according to an embodiment of the present application.

[0024] Figure 7 It is a schematic diagram of the main structure of an intelligent device according to an embodiment of the present application. Detailed implementation manners

[0025] The following describes some implementation manners of the present application with reference to the accompanying drawings. Those skilled in the art should understand that these implementation manners are only used to explain the technical principle of the present application and are not intended to limit the protection scope of the present application.

[0026] In the description of the present application, "module" and "processor" may include hardware, software, or a combination of both. A module may include a hardware circuit, various suitable sensors, communication ports, memories, and may also include a software part, such as program code, or a combination of software and hardware. A processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, in hardware, or in a combination of both. A computer-readable storage medium includes any suitable medium for storing program code, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, and so on. The term "A and / or B" represents all possible combinations of A and B, such as only A, only B, or A and B. The term "at least one A or B" or "at least one of A and B" has a meaning similar to "A and / or B" and may include only A, only B, or A and B. The singular terms "a" and "this" may also include the plural form.

[0027] Refer to the attached Figure 1 , Figure 1 It is a schematic diagram of the main step flow of a motor fault diagnosis method according to an embodiment of the present application. As Figure 1 shown, the motor fault diagnosis method in the embodiment of the present application includes: Step S101: Obtain the three-phase voltage signal sequence and the three-phase current signal sequence of the motor; Step S102: Obtain the three-phase voltage fundamental frequency signal sequence based on the three-phase voltage signal sequence, and obtain the three-phase current fundamental frequency signal sequence based on the three-phase current signal sequence; Step S103: Obtain the a-phase negative sequence component sequence of the voltage fundamental frequency signal based on the three-phase voltage fundamental frequency signal sequence, and obtain the a-phase negative sequence component sequence of the current fundamental frequency signal based on the three-phase current fundamental frequency signal sequence; Step S104: Based on the a-phase negative sequence component sequence of the voltage fundamental frequency signal and the a-phase negative sequence component sequence of the current fundamental frequency signal, obtain the statistical mean value of the negative sequence impedance within a preset time length through convolution; Step S105: Determine whether the motor has a turn-to-turn short circuit fault based on the statistical mean of the negative sequence impedance and a preset negative sequence impedance threshold.

[0028] In the embodiment of the present application, the wiring method of the three-phase asynchronous motor is star connection. In step S101, according to the preset sampling frequency f s and the preset sampling duration T1, the stator voltage and current of the motor are synchronously collected through a voltage sensor and a current sensor respectively, and three-phase (phase a, phase b, and phase c) voltage signal sequences {u a (n′)}, {u b (n′)}, and {u c (n′)}, and three-phase (phase a, phase b, and phase c) current signal sequences {i a (n′)}, {i b (n′)}, and {i c (n′)} are obtained. Wherein, n′ = 0, 1, 2, …, (N′ - 1), and N′ is determined by the sampling frequency f s and the sampling duration T1.

[0029] As an example, the sampling frequency f s is set to 12.8KHz, and the sampling duration T1 is set to 3 seconds. At this time, the data lengths of each phase voltage signal sequence and each phase current signal sequence are both T1 * f s = 38400, that is, data including 38400 sampling points.

[0030] In step S102, the method for obtaining the voltage fundamental frequency signal and the current fundamental frequency signal is not limited in the present application. As an example, methods such as the Kalman filtering algorithm and the Park vector method can be used to obtain the voltage fundamental frequency signal and the current fundamental frequency signal.

[0031] {u a (n′)} corresponds to the phase a voltage fundamental frequency signal sequence {u a0 (n)}, {u b (n′)} corresponds to the phase b voltage fundamental frequency signal sequence {u b0 (n)}, {u c (n′)} corresponds to the phase c voltage fundamental frequency signal sequence {u c0 (n)}; {i a (n′)} corresponds to the phase a current fundamental frequency signal sequence {i a0 (n)}, {i b (n′)} corresponds to the phase b current fundamental frequency signal sequence {i b0 (n)}, {i c (n′)} corresponds to the phase c current fundamental frequency signal sequence {i c0 (n)}, where n = 0, 1, 2, …, (N - 1).

[0032] Considering that the base-2 fast Fourier (inverse) transform requires the amount of data to be processed to be an integer power of 2, therefore, N can be selected as the value corresponding to an integer power of 2 that is less than N′. For example, N = 2 15 = 32768.

[0033] It should be noted that {u a0 (n)}, {u b0 (n)}, {u c0 (n)}, {i a0 (n)}, {i b0 (n)} and {i c0 (n)} need to be continuously selected synchronously, that is, the sampling times corresponding to the sequence elements with the same serial number in {u a0 (n)}, {u b0 (n)}, {u c0 (n)}, {i a0 (n)}, {i b0 (n)} and {i c0 (n)} should be the same.

[0034] Step S103 specifically includes: based on the three-phase voltage fundamental frequency signal sequence, through fast Fourier transform and considering spectral leakage, obtaining the negative sequence component sequence of the a-phase of the voltage fundamental frequency signal; based on the three-phase current fundamental frequency signal sequence, through fast Fourier transform and considering spectral leakage, obtaining the negative sequence component sequence of the a-phase of the current fundamental frequency signal.

[0035] Continue reading Figure 2 , to Figure 2 illustrate the detailed step flow of step S103. Figure 2 is a schematic diagram of the detailed step flow of step S103 according to an embodiment of the present application.

[0036] In step S201, fast Fourier transforms are respectively performed on the three-phase fundamental frequency signal sequences to obtain three-phase fundamental frequency domain signal sequences.

[0037] The three-phase fundamental frequency signal sequences include three-phase voltage fundamental frequency signal sequences and three-phase current fundamental frequency signal sequences. Correspondingly, the three-phase fundamental frequency domain signal sequences include three-phase voltage fundamental frequency domain signal sequences and three-phase current fundamental frequency domain signal sequences.

[0038] Specifically, fast Fourier transforms are respectively performed on the three-phase voltage fundamental frequency signal sequences (the a-phase voltage fundamental frequency signal sequence {u a0 (n)}, the b-phase voltage fundamental frequency signal sequence {u b0 (n)} and the c-phase voltage fundamental frequency signal sequence {u c0 (n)}) to obtain three-phase voltage fundamental frequency domain signal sequences.

[0039] The three-phase voltage fundamental frequency domain signal sequence includes: the a-phase voltage fundamental frequency domain signal sequence is {u af (n)}, the b-phase voltage fundamental frequency domain signal sequence is {u bf (n)}, the c-phase voltage fundamental frequency domain signal sequence is {u cf (n)}, where n = 0, 1, 2, …, (N - 1).

[0040] Perform fast Fourier transform on the three-phase current fundamental frequency signal sequences (the a-phase current fundamental frequency signal sequence {i a0 (n)}, the b-phase current fundamental frequency signal sequence {i b0 (n)}, and the c-phase current fundamental frequency signal sequence {i c0 (n)}) respectively to obtain the three-phase current fundamental frequency domain signal sequence.

[0041] The three-phase current fundamental frequency domain signal sequence includes: the a-phase current fundamental frequency domain signal sequence is {i af (n)}, the b-phase current fundamental frequency domain signal sequence is {i bf (n)}, the c-phase current fundamental frequency domain signal sequence is {i cf (n)}, where n = 0, 1, 2, …, (N - 1).

[0042] In step S202, based on the three-phase voltage fundamental frequency domain signal sequence, obtain the frequency domain signal sequence of the negative sequence component of the a-phase of the voltage fundamental frequency signal through the conversion vector.

[0043] Continue reading Figure 3 , through Figure 3 Describe the detailed step process of step S202. Figure 3 It is a schematic diagram of the detailed step process of step S202 according to an embodiment of the present application.

[0044] In step S301, based on the fundamental frequency point number, symmetrically extract a preset number of sequence elements from the three-phase voltage fundamental frequency domain signal sequence to obtain the three-phase voltage fundamental frequency domain signal basis matrix, where the fundamental frequency point number is determined based on the fundamental frequency and the spectral resolution, and the preset number is determined based on spectral leakage.

[0045] As shown in the appendix Figure 4 shown, Figure 4 (a) is the amplitude spectrum diagram obtained by performing fast Fourier transform on the a-phase voltage time domain signal according to an embodiment of the present application. Wherein, the vertical coordinate is the voltage amplitude, the lower horizontal coordinate is the frequency, and the upper horizontal coordinate is the serial number value corresponding to each frequency point.

[0046] In the embodiment of the present application, the number N of input data for performing fast Fourier transform on the voltage fundamental frequency signal sequences of phase a, phase b, and phase c is 32768, and the sampling frequency f s is 12.8 KHz. At this time, the spectral resolution of the Fourier transform is f s / N.

[0047] The fundamental frequency f e The corresponding fundamental frequency point number is f e / (f s / N). In the embodiment of the present application, the fundamental frequency f e = 50 Hz. At this time, the fundamental frequency point number f e / (f s / N) = 50 / (12800 / 32768) = 128.

[0048] Considering spectral leakage, in the voltage fundamental frequency domain signal sequences {u af (n)} of phase a, {u bf (n)} of phase b, and {u cf (n)} of phase c, centered on the fundamental frequency point number 128, symmetrically extract a preset number (127, and the value range of n is [65, 191], that is, the range of spectral leakage of the fundamental frequency can generally be considered not to exceed the range of the value where the sequence numbers at both ends decrease / increase by half of the fundamental frequency point number centered on the fundamental frequency point number. In the embodiment of the present application, the decreased / increased value is 63) of elements to form 3 groups of 1x127 row vectors, and obtain the three-phase voltage fundamental frequency domain signal basis matrix Bu from these 3 groups of 1x127 row vectors:

[0049] In step S302, based on the three-phase voltage fundamental frequency domain signal basis matrix and the conversion vector, obtain the frequency domain basis vector of the negative sequence component of the voltage fundamental frequency signal of phase a.

[0050] First, construct a 1x3 conversion vector S, where,

[0051] Then, according to the three-phase voltage fundamental frequency domain signal basis matrix Bu and the conversion vector S, calculate the elements of the fast Fourier transform result of the negative sequence component of the voltage fundamental frequency signal of phase a whose sequence numbers are between [65, 191] to obtain the frequency domain basis vector U anf = SBu, where SBu represents the dot product of the row vector S and the matrix Bu, and U anf is a 1x127 row vector.

[0052] In step S303, the frequency-domain basis vector U of the negative-sequence component of the a-phase of the voltage fundamental-frequency signal anf is expanded to obtain a frequency-domain signal sequence of the negative-sequence component of the a-phase of the voltage fundamental-frequency signal (i.e., the fast Fourier transform result of the negative-sequence component of the a-phase of the voltage fundamental-frequency signal, with a total of 32,768 data).

[0053] Based on U anf , four groups of row vectors U anf1 , U anf2 , U anf3 and U anf4 are constructed. According to the characteristics of the spectrum obtained by Fourier transform, the numerical values of the sequence elements in U anf1 , U anf2 and U anf4 are all 0, the number of sequence elements corresponding to U anf1 is 65, the number of sequence elements corresponding to U anf2 is 32,385, and the number of sequence elements corresponding to U anf4 is 64.

[0054] For U anf3 , first, the conjugate complex numbers of each sequence element in U anf are obtained to get the conjugate row vector anf of U Then, after reversing all the sequence elements in , U anf3 is obtained, where the sequence element number range in U anf3 is [32,768 - 191, 32,768 - 65], that is, [32,577, 32,703].[[]END]

[0055] As an example, U anf = [a + bi... c + di], the sequence number corresponding to a + bi is 65, the conjugate row vector anf of U Then, after reversing all the sequence elements in , U anf3 = [c - di... a - bi], where the sequence number corresponding to c - di is 32,768 - 191, that is, 32,577, and the sequence number corresponding to a - bi is 32,768 - 65, that is, 32,703.

[0056] According to U anf1 , U anf , U anf2 , U anf3 and U anf4Combine these 5 row vectors in a certain order to obtain a 1x32768 row vector, which is the frequency-domain signal sequence of the negative-sequence component of the a-phase of the voltage fundamental-frequency signal that retains the fundamental-frequency fluctuation characteristics, and is also the Fourier transform result of the negative-sequence component of the a-phase of the voltage fundamental-frequency signal.

[0057] In step S203, perform an inverse fast Fourier transform on the frequency-domain signal sequence of the negative-sequence component of the a-phase of the voltage fundamental-frequency signal obtained in the above step S303, and then take the real part of the inverse fast Fourier transform result to obtain the negative-sequence component sequence {u ′ (n)} of the a-phase of the voltage fundamental-frequency signal, where n = 0, 1, 2, …, (N - 1).

[0058] In step S204, based on the three-phase current fundamental-frequency domain signal sequence, obtain the frequency-domain signal sequence of the negative-sequence component of the a-phase of the current fundamental-frequency signal through a conversion vector.

[0059] Continue reading Figure 5 , through Figure 5 Describe the detailed step flow of step S204. Figure 5 is a schematic diagram of the detailed step flow of step S204 according to an embodiment of the present application.

[0060] In step S501, based on the fundamental-frequency point number, symmetrically extract a preset number of sequence elements from the three-phase current fundamental-frequency domain signal sequence to obtain the three-phase current fundamental-frequency domain signal basis matrix, where the fundamental-frequency point number is determined based on the fundamental frequency and the spectral resolution, and the preset number is determined based on spectral leakage.

[0061] As shown in the appendix Figure 4 shown, Figure 4 (b) is the amplitude spectrum obtained by performing a fast Fourier transform on the a-phase current time-domain signal according to an embodiment of the present application, where the ordinate is the current amplitude, the lower abscissa is the frequency, and the upper abscissa is the serial number value corresponding to each frequency point.

[0062] In the embodiment of the present application, the number N of the input data for performing the fast Fourier transform on the current fundamental-frequency signal sequences of the a-phase, b-phase, and c-phase is also 32768, and the sampling frequency f s is also 12.8KHz. At this time, the spectral resolution of the Fourier transform is f s / N.

[0063] The fundamental-frequency point number corresponding to the fundamental frequency f e is f e / (f s / N). In the embodiment of the present application, the fundamental frequency f e = 50Hz. At this time, the fundamental-frequency point number f e / (fs / N) = 50 / (12800 / 32768) = 128.

[0064] Similarly, considering spectral leakage, in the fundamental frequency domain signal sequences of phase-a current {i af (n)}, the fundamental frequency domain signal sequence of phase-b current {i bf (n)}, and the fundamental frequency domain signal sequence of phase-c current {i cf (n)}, centered on the fundamental frequency point number 128, symmetrically extract a preset number (127, where the value range of n is [65, 191], that is, the range of fundamental frequency spectral leakage can generally be considered not to exceed the range with the fundamental frequency point number as the center and the numbers at both ends reduced / increased by half of the fundamental frequency point number. In the embodiments of the present application, the reduced / increased value is 63) of elements to form 3 groups of 1x127 row vectors, and obtain the fundamental matrix of three-phase current fundamental frequency domain signals, Bi:

[0065] In step S502, based on the fundamental matrix of three-phase current fundamental frequency domain signals and the conversion vector, obtain the frequency domain fundamental vector of the negative sequence component of phase-a of the current fundamental frequency signal.

[0066] First, construct a 1x3 conversion vector S, where,

[0067] Then, according to the fundamental matrix Bi of three-phase current fundamental frequency domain signals and the conversion vector S, calculate the elements of the fast Fourier transform result of the negative sequence component of phase-a of the current fundamental frequency signal whose sequence numbers are between [65, 191] to obtain the frequency domain fundamental vector I anf = SBi, where SBi represents the dot product of the row vector S and the matrix Bi, and I anf is a 1x127 row vector.

[0068] In step S503, expand the frequency domain fundamental vector I anf of the negative sequence component of phase-a of the current fundamental frequency signal to obtain the frequency domain signal sequence of the negative sequence component of phase-a of the current fundamental frequency signal (that is, the fast Fourier transform result of the negative sequence component of phase-a of the current fundamental frequency signal, with a total of 32768 data).

[0069] Based on I anf , construct 4 groups of row vectors I anf1 , I anf2 , I anf3 and I anf4 . According to the characteristics of the spectrum obtained by Fourier transform, I anf1 , Ianf2 and I anf4 The numerical values of the sequence elements in are all 0, I anf1 The corresponding number of sequence elements is 65, I anf2 The corresponding number of sequence elements is 32385, I anf4 The corresponding number of sequence elements is 64.

[0070] For I anf3 , first obtain I anf The conjugate complex numbers of each sequence element in, to obtain I anf The conjugate row vector of Then for After performing reverse order processing on all the sequence elements in, to obtain I anf3 , where I anf3 The serial number range of the sequence elements in is [32768 - 191, 32768 - 65], that is, [32577, 32703].

[0071] As an example, I anf =[a + bi…c + di], the serial number corresponding to a + bi is 65, I anf The conjugate row vector of Then for After performing reverse order processing on all the sequence elements in, to obtain I anf3 =[c - di…a - bi], where, the serial number corresponding to c - di is 32768 - 191, that is, 32577, and the serial number corresponding to a - bi is, 32768 - 65, that is, 32703.

[0072] According to I anf1 , I anf , I anf2 , I anf3 and I anf4 Combine these 5 row vectors in the order of, to obtain a 1x32768 row vector, which is the frequency domain signal sequence of the negative sequence component of the a-phase of the current fundamental frequency signal that retains the fundamental frequency fluctuation characteristics, and is also the Fourier transform result of the negative sequence component of the a-phase of the current fundamental frequency signal.

[0073] In step S205, perform an inverse fast Fourier transform on the frequency domain signal sequence of the negative sequence component of the a-phase of the current fundamental frequency signal obtained in the above step S503, and then take the real part of the inverse fast Fourier transform result to obtain the negative sequence component sequence {i ′ (n)} of the a-phase of the current fundamental frequency signal, where, n = 0, 1, 2, …, (N - 1).

[0074] Through the method of the present application for obtaining the negative sequence component of the a-phase of the voltage / current fundamental frequency signal based on Fourier transform and simultaneously considering spectral leakage, compared with the traditional method for estimating the negative sequence component of the voltage / current fundamental frequency signal based on phasor method, the method of the present application can effectively retain the fundamental frequency fluctuation characteristics of the power grid voltage / current signal, improve the extraction accuracy of the negative sequence component of the voltage / current fundamental frequency signal, and thus make the calculation of impedance more accurate.

[0075] Next, in combination with Figure 6 , a method for obtaining the statistical mean value of the negative sequence impedance will be described. Figure 6 FIG. is a detailed step flow diagram of step S104 according to an embodiment of the present application.

[0076] In step S601, the negative sequence component sequence of the voltage fundamental frequency signal is reconstructed and expanded to obtain the voltage fundamental frequency negative sequence component sequence {u(k)}, where k = 0, 1, 2,..., N.

[0077] Specifically, u ′ (0) is filled to the very front of all elements in {u ′ (n)}, and then the voltage fundamental frequency negative sequence component sequence {u(k)} can be obtained, where {u(k)} = {u ′ (0), u ′ (0), u ′ (1), …, u ′ (N - 1)}, and at this time, the number of elements in {u(k)} is (N + 1), that is, 32769.

[0078] In step S602, the sequence number of the negative sequence component sequence of the current fundamental frequency signal is reconstructed to obtain the current fundamental frequency negative sequence component sequence {i(k)}, where k = 1, 2,..., N.

[0079] Specifically, the sampling moments of all elements in {i ′ (n)} are shifted backward by one sampling interval, and then the current fundamental frequency negative sequence component sequence {i(k)} can be obtained, where i(1) = i′(0), i(N) = i′(N - 1), and at this time, the number of elements in {i(k)} is still N, that is, 32768.

[0080] In step S603, based on the voltage fundamental frequency negative sequence component sequence, the current fundamental frequency negative sequence component sequence, and the parameters of the convolution kernel, the statistical mean value of the negative sequence impedance within a preset duration is obtained. Among them, the convolution kernel is obtained according to the convolution equation between the sinusoidal voltage signal and the sinusoidal current signal.

[0081] In the embodiment of the present application, the preset duration is the time length corresponding to the current fundamental frequency negative sequence component sequence {i(k)}, that is, the preset duration is N / f s= 32768 / 12800 = 2.56 seconds, that is, the statistical mean value of the negative-sequence impedance within a time length of 2.56 seconds is obtained.

[0082] The difference between the voltage fundamental-frequency signal and the current fundamental-frequency signal of an induction motor is caused by the impedance of the induction motor. Since convolution can adjust the amplitude and phase of a sine signal, the relationship between the voltage fundamental-frequency signal (sine voltage signal) and the current fundamental-frequency signal (sine current signal) can be expressed by the following convolution equation: Among them, h[m] (m takes any integer) constitutes the convolution kernel H.

[0083] Since the voltage fundamental-frequency signal and the current fundamental-frequency signal in this application have causality, when m takes a negative integer, h[m] = 0. Considering that convolution with a step size of 2 can achieve the adjustment of the amplitude and phase of the voltage fundamental-frequency signal, in the embodiments of this application, the convolution kernel H selects the convolution kernel H0 with 2 parameters, that is, H0 = [h[0] h[1]].

[0084] In the embodiments of this application, according to Equation 1, when the convolution kernel H0 is selected, the voltage fundamental-frequency negative-sequence component sequence {u(k)} and the current fundamental-frequency negative-sequence component sequence {i(k)} within the preset time length have the following relationship:

[0085] Multiply both sides of Equation 2 by the voltage fundamental-frequency negative-sequence component matrix of Nx2, and we can get:

[0086] That is,

[0087] Divide both sides of Equation 3 by N, and we can get:

[0088] According to Equation 4, the parameters h[0] and h[1] of the convolution kernel H0 within the preset time length can be obtained, that is,

[0089] Based on the obtained parameters h[0] and h[1] of the convolution kernel H0, and then based on Equation 1, the relationship between the voltage fundamental-frequency signal and the current fundamental-frequency signal within the preset time length is visualized, and we get: Among them, A u is the amplitude of the voltage fundamental-frequency signal, A i is the amplitude of the current fundamental-frequency signal, θ is the phase angle of the voltage signal, θ IMThe phase difference between the voltage signal and the current signal caused by impedance.

[0090] After performing trigonometric expansion on the left side of Equation 5, it becomes:

[0091] After performing trigonometric expansion on the right side of Equation 5, it becomes:

[0092] For any n, Equation 6 and Equation 7 are equal. Therefore, the following equation holds:

[0093] Adding the squared results of Equation 8 and Equation 9 respectively gives: Where, is the statistical mean of the negative-sequence impedance within the preset time duration.

[0094] It should be noted that in other embodiments, those skilled in the art can also select other values for the convolution step size. For example, the value of the convolution step size is 3, 4, or other values. In this case, the statistical mean of the negative-sequence impedance within the preset time duration can also be obtained through convolution. Without departing from the principle of the present application, these modified solutions are equivalent technical solutions to the technical solutions described in the present application and will therefore also fall within the protection scope of the present application.

[0095] In step S105, obtain the impedance change ratio between the statistical mean of the negative-sequence impedance and the negative-sequence impedance threshold. Specifically,

[0096] When the statistical mean of the negative-sequence impedance is less than the negative-sequence impedance threshold, based on the preset impedance change interval, determine the inter-turn short-circuit fault, where the impedance change interval can be one or multiple.

[0097] When the impedance change interval [q, 1) is one, when the statistical mean of the negative-sequence impedance belongs to the impedance change interval [q, 1), it is determined that the motor has an inter-turn short-circuit fault. As an example, q can be set to 20%.

[0098] When the impedance change interval is multiple, including impedance change interval 1 [q1, q2) and impedance change interval 2 [q2, 1), at this time, each impedance change interval corresponds to a fault type of the inter-turn short-circuit fault.

[0099] When the statistical mean of the negative-sequence impedance belongs to the impedance change interval 1 [q1, q2), it is determined that the motor has a turn-to-turn short-circuit fault, and the fault type is a minor fault; when the statistical mean of the negative-sequence impedance belongs to the impedance change interval 2 [q2, 1), it is determined that the motor has a turn-to-turn short-circuit fault, and the fault type is a severe fault. As an example, q1 can be set to 20% and q2 can be set to 40%.

[0100] In the embodiments of the present application, the negative-sequence impedance threshold can be set according to the historical data of the impedance measurement of the asynchronous motor. For example, the impedance mean value within a certain historical time period. And, as the asynchronous motor is used, the negative-sequence impedance threshold is updated regularly to obtain a negative-sequence impedance threshold that can reflect the latest condition of the stator of the asynchronous motor.

[0101] The present application calculates the statistical mean of the negative-sequence impedance within a preset time period through convolution, reduces the volatility of the calculation result of the negative-sequence impedance, and improves the reliability of the motor turn-to-turn short-circuit fault diagnosis based on the negative-sequence impedance.

[0102] On the other hand, the present application also provides an intelligent device.

[0103] In an embodiment of an intelligent device according to the present application, the intelligent device may include at least one processor; and a memory communicatively connected to the at least one processor; wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the motor fault diagnosis method described in any of the above embodiments is implemented. Refer to the attached Figure 7 , Figure 7 It is exemplarily shown in the figure that the intelligent device 7 includes a memory 71 and a processor 72, and the memory 71 and the processor 72 are communicatively connected through a bus.

[0104] Furthermore, the present application also provides a storage medium.

[0105] In an embodiment of a storage medium according to the present application, the storage medium can be configured to store a program for executing the motor fault diagnosis method in the above method embodiments, and the program can be loaded and run by a processor to implement the above motor fault diagnosis method. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown. For the specific technical details not disclosed, please refer to the method part of the embodiments of the present application. The storage medium can be a storage device formed by various electronic devices. As an example, the storage medium in the embodiments of the present application is a non-transitory storage medium.

[0106] Those skilled in the art should be able to realize that although the various steps are described in a specific order in the above embodiments, those skilled in the art can understand that in order to achieve the effects of this application, it is not necessary for different steps to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These adjusted solutions are equivalent technical solutions to the technical solutions described in this application, and therefore will also fall within the protection scope of this application.

[0107] So far, the technical solution of this application has been described in conjunction with an embodiment shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of this application is obviously not limited to these specific embodiments. Without departing from the principle of this application, those skilled in the art can make equivalent changes or substitutions to relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of this application.

Claims

1. A motor fault diagnosis method, characterized in that: The method comprises: Obtaining a three-phase voltage signal sequence and a three-phase current signal sequence of the motor; Acquire a three-phase voltage fundamental frequency signal sequence based on the three-phase voltage signal sequence, and acquire a three-phase current fundamental frequency signal sequence based on the three-phase current signal sequence, wherein the time lengths corresponding to the three-phase voltage fundamental frequency signal sequence and the three-phase current fundamental frequency signal sequence are both preset time lengths; Based on the three-phase voltage fundamental frequency signal sequence, an a-phase negative-sequence component sequence of the voltage fundamental frequency signal is obtained; based on the three-phase current fundamental frequency signal sequence, an a-phase negative-sequence component sequence of the current fundamental frequency signal is obtained; Based on the a-phase negative-sequence component sequence of the voltage fundamental frequency signal and the a-phase negative-sequence component sequence of the current fundamental frequency signal, a statistical mean value of the negative-sequence impedance within the preset time length is obtained by convolution; Based on the statistical mean value of the negative-sequence impedance and a preset negative-sequence impedance threshold, it is determined whether a turn-to-turn short circuit fault occurs in the motor.

2. The motor fault diagnosis method according to claim 1, characterized in that: The method further comprises: Based on the three-phase voltage baseband signal sequence, a negative sequence component sequence of the a-phase of the voltage baseband signal is obtained by fast Fourier transform and considering spectrum leakage; Based on the three-phase current fundamental frequency signal sequence, a phase a negative sequence component sequence of the current fundamental frequency signal is obtained through fast Fourier transform and considering spectrum leakage.

3. The motor fault diagnosis method according to claim 2, characterized in that: The method further comprises: Performing fast Fourier transform on the three-phase baseband signal sequences respectively to obtain three-phase baseband frequency domain signal sequences, wherein the three-phase baseband signal sequences include the three-phase voltage baseband signal sequences and the three-phase current baseband signal sequences, and the three-phase baseband frequency domain signal sequences include the three-phase voltage baseband frequency domain signal sequences and the three-phase current baseband frequency domain signal sequences; Based on the three-phase voltage fundamental frequency domain signal sequence, a frequency domain signal sequence of the a-phase negative-sequence component of the voltage fundamental frequency signal is obtained by converting the vector, and an inverse fast Fourier transform is performed on the frequency domain signal sequence of the a-phase negative-sequence component of the voltage fundamental frequency signal to obtain a negative-sequence component sequence of the a-phase of the voltage fundamental frequency signal; Based on the three-phase current fundamental frequency domain signal sequence, a frequency domain signal sequence of the a-phase negative-sequence component of the current fundamental frequency signal is obtained by converting the vector, and an inverse fast Fourier transform is performed on the frequency domain signal sequence of the a-phase negative-sequence component of the current fundamental frequency signal to obtain a sequence of the a-phase negative-sequence component of the current fundamental frequency signal.

4. The motor fault diagnosis method according to claim 3, characterized in that: "Based on the three-phase voltage fundamental frequency domain signal sequence, obtaining the frequency domain signal sequence of the a-phase negative sequence component of the voltage fundamental frequency signal by converting the vector" includes: Based on the fundamental frequency point sequence number, a preset number of sequence elements are symmetrically extracted from the three-phase voltage fundamental frequency domain signal sequence to obtain a three-phase voltage fundamental frequency domain signal basic matrix, wherein the fundamental frequency point sequence number is determined based on the fundamental frequency and the spectrum resolution, and the preset number is determined based on the spectrum leakage; Based on the three-phase voltage fundamental frequency domain signal basic matrix and the conversion vector, obtaining the frequency domain basic vector of the a-phase negative sequence component of the voltage fundamental frequency signal; The frequency domain basis vector of the a-phase negative-sequence component of the voltage baseband signal is expanded to obtain a frequency domain signal sequence of the a-phase negative-sequence component of the voltage baseband signal.

5. The motor fault diagnosis method according to claim 3, characterized in that: "Based on the three-phase current fundamental frequency domain signal sequence, obtaining the frequency domain signal sequence of the a-phase negative sequence component of the current fundamental frequency signal by converting the vector" includes: Based on the fundamental frequency point sequence number, a preset number of sequence elements are symmetrically extracted from the three-phase current fundamental frequency domain signal sequence to obtain a three-phase current fundamental frequency domain signal basic matrix, wherein the fundamental frequency point sequence number is determined based on the fundamental frequency and the spectrum resolution, and the preset number is determined based on the spectrum leakage; Based on the three-phase current fundamental frequency domain signal basic matrix and the conversion vector, obtaining the frequency domain basic vector of the a-phase negative sequence component of the current fundamental frequency signal; The frequency domain basis vector of the a-phase negative sequence component of the current fundamental frequency signal is expanded to obtain a frequency domain signal sequence of the a-phase negative sequence component of the current fundamental frequency signal.

6. The motor fault diagnosis method according to claim 1, characterized in that: “Acquiring the statistical mean of the negative-sequence impedance within the preset time length by convolution based on the a-phase negative-sequence component sequence of the voltage fundamental frequency signal and the a-phase negative-sequence component sequence of the current fundamental frequency signal” includes: Reconstructing and expanding the a-phase negative-sequence component sequence of the voltage fundamental frequency signal to obtain a voltage fundamental frequency negative-sequence component sequence; Reconstructing the sequence number of the a-phase negative-sequence component sequence of the current fundamental frequency signal to obtain a current fundamental frequency negative-sequence component sequence; Based on the voltage fundamental frequency negative sequence component sequence, the current fundamental frequency negative sequence component sequence and the parameters of the convolution kernel, the statistical mean of the negative sequence impedance is obtained, wherein the parameters of the convolution kernel are obtained according to the convolution equation between the sinusoidal voltage signal and the sinusoidal current signal.

7. The motor fault diagnosis method according to claim 6, characterized in that: The method further comprises: The convolution equation is constructed based on a convolution step size of 2.

8. The motor fault diagnosis method according to claim 1, characterized in that: “Judging whether a turn-to-turn short circuit fault occurs in the motor based on the statistical mean value of the negative-sequence impedance and a preset negative-sequence impedance threshold” includes: Obtaining an impedance change ratio between a statistical mean value of the negative-sequence impedance and the negative-sequence impedance threshold; When the statistical mean of the negative-sequence impedance is less than the negative-sequence impedance threshold, the turn-to-turn short circuit fault is determined based on a preset impedance variation interval.

9. A smart device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; Wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the motor fault diagnosis method according to any one of claims 1 to 8 is implemented.

10. A storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the motor fault diagnosis method according to any one of claims 1 to 8.

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