Motor fault diagnosis method, intelligent device and storage medium

Abstract

The application relates to the motor technical field, in particular to a motor fault diagnosis method, an intelligent device and a storage medium, and aims to solve the technical problems of how to accurately obtain the negative sequence component of a voltage / current fundamental frequency signal based on fast Fourier transform and spectrum leakage, and how to obtain the statistical mean of the negative sequence impedance based on convolution to carry out motor fault diagnosis. Based on a three-phase voltage / current fundamental frequency signal sequence, the a-phase negative sequence component of the voltage / current fundamental frequency signal is obtained through fast Fourier transform and according to spectrum leakage, the fundamental frequency fluctuation characteristics of the power grid voltage / current signal can be effectively retained, the extraction accuracy of the negative sequence component of the voltage / current fundamental frequency signal is improved, and therefore the impedance calculation is more accurate; the statistical mean of the negative sequence impedance in a preset time length is calculated through convolution, the volatility of the negative sequence impedance calculation result is reduced, and the reliability of the motor turn-to-turn short-circuit fault diagnosis based on the negative sequence impedance threshold value is improved.

Description

Technical Field

[0001] The present application relates to the field of motor technology, and in particular to a motor fault diagnosis method, an intelligent device, and a storage medium. Background Art

[0002] Turn-to-turn short-circuit faults, second only to bearing failures in the incidence of asynchronous motor failures, pose a serious threat to the safe and stable operation of asynchronous motors. Therefore, real-time online diagnosis of turn-to-turn short-circuit faults in asynchronous motors is of great significance. Currently, negative-sequence impedance analysis (NSEA) is a mainstream online diagnosis method for turn-to-turn short-circuit faults. This method primarily uses the negative-sequence impedance of an asynchronous motor to assess the imbalance of its three-phase electrical parameters and, in turn, diagnose the turn-to-turn short-circuit fault.

[0003] In the diagnosis of turn-to-turn short-circuit faults in asynchronous motors, the negative-sequence impedance analysis method, while simple and straightforward, is not ideal due to numerous interfering factors. In practice, due to the difficulty in accurately extracting the stator line current fundamental frequency signal, the negative-sequence impedance is often directly calculated using the voltage and stator line current signals, which inevitably leads to deviations in the calculated results. Furthermore, due to the volatility of the voltage signal, the impedance calculated using the phasor method also exhibits significant volatility, which can interfere with threshold determination. Therefore, improving the reliability of the negative-sequence impedance analysis method for diagnosing turn-to-turn short-circuit faults has become an urgent issue.

[0004] Accordingly, the art needs a new motor fault diagnosis solution to solve the above problems. Summary of the Invention

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

[0006] In a first aspect, a motor fault diagnosis method is provided, the method comprising:

[0007] Obtaining a three-phase voltage signal sequence and a three-phase current signal sequence of the motor;

[0008] 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;

[0009] Acquire a phase a negative sequence component sequence of the voltage fundamental frequency signal based on the three-phase voltage fundamental frequency signal sequence, and acquire a phase a negative sequence component sequence of the current fundamental frequency signal based on the three-phase current fundamental frequency signal sequence;

[0010] Obtaining a 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;

[0011] Based on the statistical mean of the negative-sequence impedance and a preset negative-sequence impedance threshold, it is determined whether an inter-turn short circuit fault occurs in the motor.

[0012] In one technical solution of the above motor fault diagnosis method, the method further includes:

[0013] Based on the three-phase voltage fundamental frequency signal sequence, a phase a negative sequence component sequence of the voltage fundamental frequency signal is obtained by fast Fourier transform and taking into account spectrum leakage;

[0014] 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 by fast Fourier transform and taking spectrum leakage into consideration.

[0015] In one technical solution of the above motor fault diagnosis method, the method further includes:

[0016] Performing fast Fourier transform on the three-phase fundamental frequency signal sequences respectively to obtain three-phase fundamental frequency frequency domain signal sequences, wherein the three-phase fundamental frequency signal sequences include the three-phase voltage fundamental frequency signal sequence and the three-phase current fundamental frequency signal sequence, and the three-phase fundamental frequency frequency domain signal sequences include the three-phase voltage fundamental frequency frequency domain signal sequence and the three-phase current fundamental frequency frequency domain signal sequence;

[0017] Based on the three-phase voltage fundamental frequency domain signal sequence, obtaining a frequency domain signal sequence of the a-phase negative-sequence component of the voltage fundamental frequency signal by converting the vector, and performing an inverse fast Fourier transform on the frequency domain signal sequence of the a-phase negative-sequence component of the voltage fundamental frequency signal to obtain a sequence of the a-phase negative-sequence component of the voltage fundamental frequency signal;

[0018] 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.

[0019] In one technical solution of the above-mentioned motor fault diagnosis method, “obtaining a frequency domain signal sequence of the a-phase negative sequence component of the voltage fundamental frequency signal by transforming the vector based on the three-phase voltage fundamental frequency frequency domain signal sequence” includes:

[0020] Based on the fundamental frequency point sequence number, symmetrically extracting a preset number of sequence elements in 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;

[0021] Based on the three-phase voltage fundamental frequency 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;

[0022] The frequency domain basis vector of the a-phase negative sequence component of the voltage fundamental frequency signal is expanded to obtain a frequency domain signal sequence of the a-phase negative sequence component of the voltage fundamental frequency signal.

[0023] In one technical solution of the above-mentioned motor fault diagnosis method, “obtaining a frequency domain signal sequence of the a-phase negative sequence component of the current fundamental frequency signal by transforming the vector based on the three-phase current fundamental frequency frequency domain signal sequence” includes:

[0024] Based on the fundamental frequency point sequence number, symmetrically extracting a preset number of sequence elements 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;

[0025] 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;

[0026] 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.

[0027] In one technical solution of the above-mentioned motor fault diagnosis method, “obtaining the statistical mean of the negative-sequence impedance within the preset time period 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:

[0028] 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;

[0029] 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;

[0030] A statistical mean of the negative-sequence impedance is obtained based on the voltage fundamental frequency negative-sequence component sequence, the current fundamental frequency negative-sequence component sequence, and parameters of a convolution kernel, wherein the parameters of the convolution kernel are obtained according to a convolution equation between a sinusoidal voltage signal and a sinusoidal current signal.

[0031] In one technical solution of the above motor fault diagnosis method, the method further includes:

[0032] The convolution equation is constructed based on a convolution step size of 2.

[0033] In one technical solution of the above-mentioned motor fault diagnosis method, “determining whether a turn-to-turn short circuit fault occurs in the motor based on the statistical mean of the negative-sequence impedance and a preset negative-sequence impedance threshold” includes:

[0034] Obtaining an impedance change ratio between a statistical mean of the negative-sequence impedance and the negative-sequence impedance threshold;

[0035] When the statistical mean of the negative sequence impedance is less than the negative sequence impedance threshold, the inter-turn short circuit fault is determined based on a preset impedance variation interval.

[0036] In a second aspect, a smart device is provided, comprising at least one processor; and a memory communicatively connected to the at least one processor;

[0037] 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.

[0038] In a third aspect, a storage medium stores a plurality of program codes, wherein the computer program, when executed by the at least one processor, implements the method described in any one of the technical solutions of the above-mentioned motor fault diagnosis method.

[0039] One or more of the above-mentioned technical solutions of the present application have at least one or more of the following beneficial effects: a method for obtaining the a-phase negative-sequence component of the target voltage / current fundamental frequency signal based on Fourier transform and taking into account spectrum 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 impedance calculation more accurate; by convolutionally calculating the statistical mean of the negative-sequence impedance within a preset time length, the volatility of the negative-sequence impedance calculation result is reduced, and the reliability of diagnosing motor inter-turn short-circuit faults based on the negative-sequence impedance threshold is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The disclosure of this application will become more easily understood with reference to the accompanying drawings. Those skilled in the art will readily appreciate that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0041] Figure 1 It is a flowchart of the main steps of a motor fault diagnosis method according to an embodiment of the present application.

[0042] Figure 2 is a detailed step flow diagram of step S103 according to an embodiment of the present application.

[0043] Figure 3 is a detailed step flow diagram of step S202 according to an embodiment of the present application.

[0044] Figure 4 is a magnitude spectrum diagram of time domain signals of a phase voltage and a phase current according to an embodiment of the present application.

[0045] Figure 5 is a detailed step flow diagram of step S204 according to an embodiment of the present application.

[0046] Figure 6 is a detailed step flow diagram of step S104 according to an embodiment of the present application.

[0047] Figure 7 is a main structure diagram of an intelligent device according to an embodiment of the present application. DETAILED DESCRIPTION

[0048] Some embodiments of the present application will be described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present application, and are not intended to limit the protection scope of the present application.

[0049] In the description of the present application, "module" and "processor" can include hardware, software or a combination of both. A module can include hardware circuit, various suitable sensors, communication port, memory, and can also include software part such as program code, and can be a combination of software and hardware. The processor can be a central processor, microprocessor, image processor, digital signal processor or any other suitable processor. The processor has data and / or signal processing function. The processor can be implemented in software, hardware or a combination of both. The computer readable storage medium includes any suitable medium that can store program code, such as magnetic disk, hard disk, optical disk, flash memory, read-only memory, random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The term "at least one of A or B" or "at least one of A and B" has similar meaning as "A and / or B", and can include only A, only B, or A and B. The singular form of the term "one", "this" can also include plural forms.

[0050] Referring to the drawings Figure 1 , Figure 1FIG. 1 is a flow chart showing the main steps of a motor fault diagnosis method according to an embodiment of the present application. Figure 1 As shown, the motor fault diagnosis method in the embodiment of the present application includes:

[0051] Step S101: Acquire a three-phase voltage signal sequence and a three-phase current signal sequence of a motor;

[0052] Step S102: obtaining a three-phase voltage fundamental frequency signal sequence based on the three-phase voltage signal sequence, and obtaining a three-phase current fundamental frequency signal sequence based on the three-phase current signal sequence;

[0053] Step S103: obtaining a phase a negative sequence component sequence of the voltage fundamental frequency signal based on the three-phase voltage fundamental frequency signal sequence, and obtaining a phase a negative sequence component sequence of the current fundamental frequency signal based on the three-phase current fundamental frequency signal sequence;

[0054] Step S104: obtaining a statistical mean of negative-sequence impedance within a preset time period 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;

[0055] Step S105: Based on the statistical mean of the negative sequence impedance and a preset negative sequence impedance threshold, it is determined whether an inter-turn short circuit fault occurs in the motor.

[0056] In the embodiment of the present application, the connection mode of the three-phase asynchronous motor is star connection. In step S101, according to the preset sampling frequency f s With the preset sampling time T1, the motor stator voltage and current are synchronously collected through the voltage sensor and current sensor respectively to obtain the three-phase (phase a, phase b and phase c) voltage signal sequence {u a (n′)}、{u b (n′)} and {u c (n′)}, and the three-phase (phase a, phase b and phase c) current signal sequence {i a (n′)}, {i b (n′)} and {i c (n′)}. Where n′=0,1,2,…,(N′-1), N′ is determined by the sampling frequency f s and sampling time T1.

[0057] As an example, the sampling frequency f s Set to 12.8KHz, the sampling time T1 is set to 3 seconds, at this time the data length of each phase voltage signal sequence and each phase current signal sequence is T1*f s =38400, which means it includes data of 38400 sampling points.

[0058] In step S102, the method of obtaining the voltage fundamental frequency signal and the current fundamental frequency signal is not limited in this application. As an example, the voltage fundamental frequency signal and the current fundamental frequency signal can be obtained by using methods such as the Kalman filter algorithm and the Park vector method.

[0059] {u a The a-phase voltage fundamental frequency signal sequence corresponding to {u a0 (n)},{u b The b-phase voltage fundamental frequency signal sequence corresponding to {u (n′)} is b0 (n)},{u c The c-phase voltage fundamental frequency signal sequence corresponding to {u (n′)} is c0 (n)}; {i a (n′)} corresponds to the fundamental frequency signal sequence of phase a current {i a0 (n)},{i b The b-phase current fundamental frequency signal sequence corresponding to {i b0 (n)},{i c The c-phase current fundamental frequency signal sequence corresponding to {i c0 (n)}, where n = 0, 1, 2, …, (N-1).

[0060] Considering that the base-2 fast Fourier (inverse) transform requires that the amount of data to be processed is an integer power of 2, N can be selected as a value corresponding to an integer power of 2 that is smaller than N′, for example, N=2 15 =32768.

[0061] 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 selected synchronously and continuously, that is, {u a0 (n)}、{u b0 (n)}、{u c0 (n)}、{i a0 (n)}、{i b0 (n)} and {i c0 The sampling moments corresponding to sequence elements with the same sequence number in (n)} should remain the same.

[0062] Step S103 specifically includes: based on the three-phase voltage fundamental frequency signal sequence, through fast Fourier transform and considering spectrum leakage, obtaining the a-phase negative sequence component sequence of the voltage fundamental frequency signal; based on the three-phase current fundamental frequency signal sequence, through fast Fourier transform and considering spectrum leakage, obtaining the a-phase negative sequence component sequence of the current fundamental frequency signal.

[0063] Continue reading Figure 2 ,pass Figure 2 The detailed process flow of step S103 is described. Figure 2 It is a detailed flowchart of step S103 according to an embodiment of the present application.

[0064] In step S201 , fast Fourier transform is performed on the three-phase fundamental frequency signal sequences respectively to obtain three-phase fundamental frequency domain signal sequences.

[0065] The three-phase fundamental frequency signal sequence includes a three-phase voltage fundamental frequency signal sequence and a three-phase current fundamental frequency signal sequence. Accordingly, the three-phase fundamental frequency domain signal sequence includes a three-phase voltage fundamental frequency domain signal sequence and a three-phase current fundamental frequency domain signal sequence.

[0066] Specifically, the three-phase voltage fundamental frequency signal sequence (a-phase voltage fundamental frequency signal sequence {u a0 (n)}, b-phase voltage fundamental frequency signal sequence {u b0 (n)} and the c-phase voltage fundamental frequency signal sequence {u c0 (n)}) performs fast Fourier transform to obtain the three-phase voltage fundamental frequency domain signal sequence.

[0067] The three-phase voltage fundamental frequency domain signal sequence includes: the a-phase voltage fundamental frequency domain signal sequence is {u af (n)}, the frequency domain signal sequence of phase b voltage fundamental frequency is {u bf (n)}, the frequency domain signal sequence of phase c voltage fundamental frequency is {u cf (n)}, where n = 0, 1, 2, …, (N-1).

[0068] The three-phase current fundamental frequency signal sequence (phase a current fundamental frequency signal sequence {i a0 (n)}, b-phase current fundamental frequency signal sequence {i b0 (n)} and the c-phase current fundamental frequency signal sequence {i c0 (n)}) performs fast Fourier transform to obtain the three-phase current fundamental frequency domain signal sequence.

[0069] The three-phase current fundamental frequency domain signal sequence includes: the a-phase current fundamental frequency domain signal sequence is {i af (n)}, the frequency domain signal sequence of the b-phase current fundamental frequency is {i bf (n)}, the frequency domain signal sequence of the phase c current fundamental frequency is {i cf (n)}, where n = 0, 1, 2, …, (N-1).

[0070] In step S202 , 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.

[0071] Continue reading Figure 3 ,pass Figure 3 The detailed process flow of step S202 is described. Figure 3 It is a detailed flowchart of step S202 according to an embodiment of the present application.

[0072] In step S301, 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.

[0073] As attached Figure 4 As shown, Figure 4 (a) is an amplitude spectrum diagram obtained by fast Fourier transform of the phase a voltage time domain signal according to one embodiment of the present application. The vertical axis is the voltage amplitude, the horizontal axis below is the frequency, and the horizontal axis above is the serial number value corresponding to each frequency point.

[0074] In the embodiment of the present application, the number of input data N for fast Fourier transform of the voltage baseband signal sequences of phase a, phase b and phase c is 32768, and the sampling frequency f is 1. s is 12.8KHz. At this time, the spectrum resolution of Fourier transform is f s / N.

[0075] 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 =50Hz, at this time, the fundamental frequency point number f e / (f s / N)=50 / (12800 / 32768)=128.

[0076] Considering spectrum leakage, the frequency domain signal sequence {u af (n)}, b-phase voltage fundamental frequency domain signal sequence {u bf (n)}, c-phase voltage fundamental frequency domain signal sequence {u cf(n)}, with the fundamental frequency point number 128 as the center, a preset number (127, the value range of n is [65, 191], that is, the range of fundamental frequency spectrum leakage can usually be considered to not exceed the range of the left and right end numbers with the fundamental frequency point number as the center, which is reduced / increased by half of the fundamental frequency point number. In the embodiment of the present application, the reduced / increased value is 63) of elements are extracted symmetrically to form three groups of 1x127 row vectors, and the three-phase voltage fundamental frequency frequency domain signal basic matrix Bu is obtained from these three groups of 1x127 row vectors:

[0077]

[0078] In step S302, based on the three-phase voltage fundamental frequency signal basic matrix and the conversion vector, the frequency domain basic vector of the a-phase negative sequence component of the voltage fundamental frequency signal is obtained.

[0079] First, construct a 1x3 transformation vector S. in,

[0080] Then, according to the three-phase voltage fundamental frequency signal basic matrix Bu and the transformation vector S, the elements of the fast Fourier transform results of the a-phase negative sequence component of the voltage fundamental frequency signal with serial numbers between [65,191] are calculated to obtain the frequency domain basic vector U of the a-phase negative sequence component of the voltage fundamental frequency signal. anf =SBu, where SBu represents the dot product of the row vector S and the matrix Bu, U anf A 1x127 row vector.

[0081] In step S303, the frequency domain basis vector U of the negative sequence component of the a phase of the voltage fundamental frequency signal is calculated. anf The frequency domain signal sequence of the negative sequence component of the phase a of the voltage fundamental frequency signal is obtained by expansion (ie, the fast Fourier transform result of the negative sequence component of the phase a of the voltage fundamental frequency signal, a total of 32768 data).

[0082] Based on U anf , construct 4 sets of row vectors U anf1 、U anf2 、U anf3 and U anf4 According to the characteristics of the spectrum obtained by Fourier transform, U anf1 、U anf2 and U anf4 The values ​​of the sequence elements in are all 0, U anf1 The corresponding number of sequence elements is 65, U anf2 The corresponding number of sequence elements is 32385, U anf4 The corresponding number of sequence elements is 64.

[0083] For Uanf3 , first obtain U anf The conjugate complex number of each sequence element in is obtained as U anf The conjugate row vector of Again After reversing all sequence elements in U anf3 , where U anf3 The sequence number range of the sequence elements in is [32768-191,32768-65], that is, [32577,32703].

[0084] As an example, U anf =[a+bi…c+di], the sequence number corresponding to a+bi is 65, U anf The conjugate row vector of Again After reversing all sequence elements in U anf3 =[c-di…a-bi], where the serial number corresponding to c-di is 32768-191, i.e. 32577, and the serial number corresponding to a-bi is 32768-65, i.e. 32703.

[0085] Follow U anf1 、U anf 、U anf2 、U anf3 and U anf4 By combining these five row vectors in the order of , a row vector of 1x32768 is obtained, which is the frequency domain signal sequence of the a-phase negative-sequence component of the voltage fundamental frequency signal that retains the fundamental frequency fluctuation characteristics. It is also the Fourier transform result of the a-phase negative-sequence component of the voltage fundamental frequency signal.

[0086] In step S203, the frequency domain signal sequence of the negative sequence component of the phase a of the voltage fundamental frequency signal obtained in step S303 is subjected to an inverse fast Fourier transform, and the real part of the inverse fast Fourier transform result is taken to obtain the negative sequence component sequence of the phase a of the voltage fundamental frequency signal {u ′ (n)}, where n = 0, 1, 2, …, (N-1).

[0087] In step S204 , based on the frequency domain signal sequence of the three-phase current fundamental frequency, a frequency domain signal sequence of the a-phase negative sequence component of the current fundamental frequency signal is obtained by converting the vector.

[0088] Continue reading Figure 5 ,pass Figure 5 The detailed process flow of step S204 is described. Figure 5 It is a detailed flowchart of step S204 according to an embodiment of the present application.

[0089] In step S501, based on the fundamental frequency point sequence number, a preset number of sequence elements are extracted symmetrically in the three-phase current fundamental frequency domain signal sequence to obtain a three-phase current fundamental frequency domain signal basis 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.

[0090] As shown in the accompanying Figure 4 , Figure 4 (b) is the amplitude spectrum of the a-phase current time domain signal obtained by fast Fourier transform according to an embodiment of the present application, wherein the vertical coordinate is the current amplitude, the lower horizontal coordinate is the frequency, and the upper horizontal coordinate is the sequence number value corresponding to each frequency point.

[0091] In the embodiment of the present application, the number N of input data for fast Fourier transform of 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 spectrum resolution of the Fourier transform is f s / N.

[0092] The fundamental frequency f e corresponds to the fundamental frequency point sequence number 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 sequence number f e / (f s / N) = 128.

[0093] Also considering the spectrum leakage, in the a-phase current fundamental frequency domain signal sequence {i af (n)}, the b-phase current fundamental frequency domain signal sequence {i bf (n)} and the c-phase current fundamental frequency domain signal sequence {i cf (n)}, a preset number (127, n is in the range of [65, 191]) of elements are extracted symmetrically around the fundamental frequency point sequence number 128 to form three 1x127 row vectors, and a three-phase current fundamental frequency domain signal basis matrix Bi is obtained from the three 1x127 row vectors:

[0094]

[0095] In step S502, based on the three-phase current fundamental frequency domain signal basis matrix and the conversion vector, a frequency domain basis vector of the a-phase negative sequence component of the current fundamental frequency signal is obtained.

[0096] First, construct a 1x3 transformation vector S. in,

[0097] Then, according to the basic matrix Bi of the three-phase current fundamental frequency domain signal and the transformation vector S, the elements with serial numbers between [65,191] of the fast Fourier transform results of the negative sequence component of the phase a of the current fundamental frequency signal are calculated to obtain the frequency domain basic vector I of the negative sequence component of the phase a of the current fundamental frequency signal. anf =SBi, where SBi represents the dot product of the row vector S and the matrix Bi, I anf A 1x127 row vector.

[0098] In step S503, the frequency domain basis vector I of the negative sequence component of the a phase of the current fundamental frequency signal is anf The frequency domain signal sequence of the negative sequence component of the a phase of the current fundamental frequency signal is obtained by expansion (ie, the fast Fourier transform result of the negative sequence component of the a phase of the current fundamental frequency signal, a total of 32768 data).

[0099] Based on I anf , construct 4 sets 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 , I anf2 and I anf4 The 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.

[0100] For I anf3 , first obtain I anf The conjugate complex number of each sequence element in I anf The conjugate row vector of Again After reversing all sequence elements in I anf3 , where I anf3 The sequence number range of the sequence elements in is [32768-191,32768-65], that is, [32577,32703].

[0101] As an example, I anf =[a+bi…c+di], the sequence number corresponding to a+bi is 65, I anf The conjugate row vector of Again After reversing all sequence elements in I anf3 =[c-di…a-bi], where the sequence number corresponding to c-di is 32768-191, i.e. 32577, and the sequence number corresponding to a-bi is 32768-65, i.e. 32703.

[0102] According to I anf1 , I anf , I anf2 , I anf3 and I anf4 By combining these five row vectors in the order of , a row vector of 1x32768 is obtained, which is the frequency domain signal sequence of the a-phase negative-sequence component of the current fundamental frequency signal that retains the fundamental frequency fluctuation characteristics. It is also the Fourier transform result of the a-phase negative-sequence component of the current fundamental frequency signal.

[0103] In step S205, the frequency domain signal sequence of the negative sequence component of the phase a of the current fundamental frequency signal obtained in step S503 is subjected to an inverse fast Fourier transform, and the real part of the inverse fast Fourier transform result is taken to obtain the negative sequence component sequence of the phase a of the current fundamental frequency signal {i ′ (n)}, where n = 0, 1, 2, …, (N-1).

[0104] Through the above-mentioned method of obtaining the a-phase negative-sequence component of the voltage / current fundamental frequency signal based on Fourier transform and taking into account spectrum leakage, compared with the traditional method of estimating the negative-sequence component of the voltage / current fundamental frequency signal based on the phasor method, the method of the present application 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 impedance calculation more accurate.

[0105] Next, combine Figure 6 , explaining the method of obtaining the statistical mean of negative sequence impedance. Figure 6 It is a detailed flowchart of step S104 according to an embodiment of the present application.

[0106] In step S601 , the negative sequence component sequence of the voltage fundamental frequency signal is reconstructed and expanded to obtain a voltage fundamental frequency negative sequence component sequence {u(k)}, where k=0, 1, 2, . . . , N.

[0107] Specifically, u ′ (0)Fill to {u ′ (n)}, we can get the voltage fundamental frequency negative sequence component sequence {u(k)}, where {u(k)}={u ′ (0),u ′ (0),u ′ (1),…,u ′(N-1)}, at this time, the number of elements in {u(k)} is (N+1), that is, 32769.

[0108] In step S602 , the sequence numbers of the negative sequence component sequences of the current fundamental frequency signal are reconstructed to obtain a current fundamental frequency negative sequence component sequence {i(k)}, where k=1, 2, . . . , N.

[0109] Specifically, {i ′ By moving the sampling moments of all elements in {i(k)} backward by one sampling interval, we can obtain the current fundamental frequency negative sequence component sequence {i(k)}, where i(1) = i′(0), i(N) = i′(N-1). At this time, the number of elements in {i(k)} is still N, that is, 32768.

[0110] In step S603, a statistical mean of negative sequence impedance within a preset time period is obtained based on the voltage fundamental frequency negative sequence component sequence, the current fundamental frequency negative sequence component sequence, and parameters of a convolution kernel, wherein the convolution kernel is obtained according to a convolution equation between the sinusoidal voltage signal and the sinusoidal current signal.

[0111] In the embodiment of the present application, the preset time length is the time length corresponding to the current fundamental frequency negative sequence component sequence {i(k)}, that is, the preset time length is N / f s =32768 / 12800=2.56 seconds, that is, the statistical mean of the negative sequence impedance within a time length of 2.56 seconds is obtained.

[0112] The difference between the voltage fundamental frequency signal and the current fundamental frequency signal of the asynchronous motor is caused by the impedance of the asynchronous motor. Since convolution can adjust the amplitude and phase of the sinusoidal signal, the relationship between the voltage fundamental frequency signal (sinusoidal voltage signal) and the current fundamental frequency signal (sinusoidal current signal) can be expressed by the following convolution equation:

[0113]

[0114] Among them, h[m] (m is any integer) constitutes the convolution kernel H.

[0115] Since the voltage fundamental frequency signal and the current fundamental frequency signal in this application are causal, when m is a negative integer, h[m] = 0. Considering that a convolution with a step size of 2 can adjust the amplitude and phase of the voltage fundamental frequency signal, in this embodiment of the application, the convolution kernel H uses the convolution kernel H0 with a parameter number of 2, that is, H0 = [h[0]h[1]].

[0116] In the embodiment of the present application, according to Formula 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:

[0117]

[0118] Multiplying both ends of Equation 2 by the Nx2 voltage fundamental frequency negative sequence component matrix, we can get:

[0119]

[0120] Right now,

[0121]

[0122] Dividing both sides of the equal sign in Equation 3 by N, we get:

[0123]

[0124] According to formula 4, the parameters h[0] and h[1] of the convolution kernel H0 within the preset time length can be obtained, that is,

[0125]

[0126] According to the obtained parameters h[0] and h[1] of the convolution kernel H0, the relationship between the voltage fundamental frequency signal and the current fundamental frequency signal within the preset time length is visualized based on formula 1, and the result is:

[0127]

[0128] 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 IM is the phase difference between the voltage signal and the current signal caused by impedance.

[0129] The trigonometric function expansion on the left side of Equation 5 is:

[0130]

[0131] The right side of Equation 5 is expanded by trigonometric functions to:

[0132]

[0133] For any n, Equations 6 and 7 are equal, so we have the following equations:

[0134]

[0135] Formula 8 and Formula 9 are squared and added together to obtain:

[0136]

[0137] in, That is, a statistical mean of the negative sequence impedance in the preset time length.

[0138] It should be noted that in other embodiments, a person skilled in the art can also select other values of the convolution step, for example, the value of the convolution step is 3, 4, or other values, at this time, the statistical mean of the negative sequence impedance in the preset time length can also be obtained by convolution. Without departing from the principles of the present application, the schemes after these changes belong to equivalent technical schemes with the technical schemes described in the present application, and therefore will also fall within the protection scope of the present application.

[0139] In step S105, the impedance change ratio between the statistical mean of the negative sequence impedance and the negative sequence impedance threshold value is obtained, specifically,

[0140]

[0141] When the statistical mean of the negative sequence impedance is less than the negative sequence impedance threshold value, the turn-to-turn short circuit fault is determined based on the preset impedance change interval, wherein the impedance change interval can be one or multiple.

[0142] 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 a turn-to-turn short circuit fault. As an example, q can be set to 20%.

[0143] 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 a turn-to-turn short circuit fault.

[0144] 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 slight 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 serious fault. As an example, q1 can be set to 20%, and q2 can be set to 40%.

[0145] In the embodiments of the present application, the negative sequence impedance threshold value can be set according to the historical data of the asynchronous motor impedance measurement, for example, the impedance mean in a period of historical time. Moreover, with the use of the asynchronous motor, the negative sequence impedance threshold value is updated regularly to obtain a negative sequence impedance threshold value that can reflect the latest condition of the stator of the asynchronous motor.

[0146] The present application calculates the statistical mean of the negative sequence impedance in the preset time length by convolution, reduces the volatility of the negative sequence impedance calculation result, and improves the reliability of the motor turn-to-turn short circuit fault diagnosis based on the negative sequence impedance.

[0147] Another aspect of the present application also provides an intelligent device.

[0148] In an embodiment of the intelligent device according to the present application, the intelligent device can comprise at least one processor; and a memory connected in communication with the at least one processor; wherein the memory has stored therein a computer program which, when executed by the at least one processor, implements the motor fault diagnosis method according to any of the above embodiments. Referring to FIG. 7, an embodiment of the intelligent device 7 is shown to comprise a memory 71 and a processor 72, which are connected in communication via a bus. Figure 7 , Figure 7 The memory 71 and the processor 72 are connected in communication via a bus.

[0149] Further, the present application also provides a storage medium.

[0150] In an embodiment of the storage medium according to the present application, the storage medium can be configured to store a program for implementing the motor fault diagnosis method according to the above method embodiments, which can be loaded and run by a processor to implement the motor fault diagnosis method according to the above embodiments. For ease of illustration, only the parts related to the embodiments of the present application are shown, and the specific technical details not disclosed are referred to the method part of the embodiments of the present application. The storage medium can be a storage device formed by various electronic devices, and as an example, the storage medium in the embodiments of the present application is a non-transitory storage medium.

[0151] Those skilled in the art should be able to understand that, although the above embodiments describe the various steps in a specific order, those skilled in the art can understand that, in order to achieve the effects of the present application, the different steps do not necessarily have to be executed in such an order, and they can be executed simultaneously (in parallel) or in other orders, and these adjusted schemes are equivalent to the technical schemes described in the present application, and thus will also fall within the protection scope of the present application.

[0152] So far, the technical scheme of the present application has been described in combination with one embodiment shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical schemes after these changes or replacements will all fall within the protection scope of the present 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; Acquire a phase a negative sequence component sequence of the voltage fundamental frequency signal based on the three-phase voltage fundamental frequency signal sequence, and acquire a phase a negative sequence component sequence of the current fundamental frequency signal based on the three-phase current fundamental frequency signal sequence; Obtaining a 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; Determining whether a turn-to-turn short circuit fault occurs in the motor based on the statistical mean of the negative-sequence impedance and a preset negative-sequence impedance threshold; The method of “obtaining a statistical mean of the negative-sequence impedance within the preset time period 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; A statistical mean of the negative-sequence impedance is obtained based on the voltage fundamental frequency negative-sequence component sequence, the current fundamental frequency negative-sequence component sequence, and parameters of a convolution kernel, wherein the parameters of the convolution kernel are obtained according to a convolution equation between a sinusoidal voltage signal and a sinusoidal current signal.

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

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 fundamental frequency signal sequences respectively to obtain three-phase fundamental frequency frequency domain signal sequences, wherein the three-phase fundamental frequency signal sequences include the three-phase voltage fundamental frequency signal sequence and the three-phase current fundamental frequency signal sequence, and the three-phase fundamental frequency frequency domain signal sequences include the three-phase voltage fundamental frequency frequency domain signal sequence and the three-phase current fundamental frequency frequency domain signal sequence; Based on the three-phase voltage fundamental frequency domain signal sequence, obtaining a frequency domain signal sequence of the a-phase negative-sequence component of the voltage fundamental frequency signal by converting the vector, and performing an inverse fast Fourier transform on the frequency domain signal sequence of the a-phase negative-sequence component of the voltage fundamental frequency signal to obtain a sequence of the a-phase negative-sequence component 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, symmetrically extracting a preset number of sequence elements in 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 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 fundamental frequency signal is expanded to obtain a frequency domain signal sequence of the a-phase negative sequence component of the voltage fundamental frequency 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, symmetrically extracting a preset number of sequence elements 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: The method further comprises: The convolution equation is constructed based on a convolution step size of 2.

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

8. 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 7 is implemented.

9. 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 7.

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