Alternating current motor fault diagnosis method and device based on modulation bispectrum, and storage medium

Abstract

The invention belongs to the technical field of motor fault diagnosis, and particularly relates to an alternating current motor fault diagnosis method and device based on modulation bispectrum and a storage medium. In order to solve the problems of signal distortion and the like in motor fault diagnosis, the method comprises the following steps of: acquiring a current signal and a voltage signal of an alternating current motor in real time, performing short-time Fourier transform on the current signal, and extracting a single-peak ridge line R1 of a time-frequency spectrogram TFS1; time-frequency analysis based on modulation bispectrum is carried out on the current signal, and a single-peak ridge R2 of a time-frequency spectrogram TFS2 is extracted; obtaining phase average power PI according to the current signal, the voltage signal and the single-peak ridge line R1; determining a characteristic frequency threshold H1 for judging whether a fault occurs or not according to the ratio of the phase average power PI to the rated power PR of the alternating current motor; acquiring a fault characteristic frequency amplitude HF according to the current signal I1 and the single-peak ridge line R2; and judging whether the AC motor has a motor broken bar fault or not and the severity of the fault according to the ratio of the fault characteristic frequency amplitude HF to the characteristic frequency threshold H1.

Description

Technical Field

[0001] This invention belongs to the field of motor fault diagnosis technology, specifically relating to AC motor fault diagnosis method, device and storage medium based on modulation bispectrum. Background Technology

[0002] Existing motor fault diagnosis methods typically determine the motor's condition by monitoring signals such as vibration, current, and voltage. Among these, vibration signal analysis is the most widely used. Its fault diagnosis relies on high-quality sensor data, but in actual working conditions, signal distortion often occurs due to noise and interference in the transmission path.

[0003] Currently, modulated bispectral frequency is used in gear and bearing fault diagnosis, but it has not yet been applied to motor fault diagnosis. Since the fundamental frequency of the current signal is the main component, the online fault diagnosis method based on modulated bispectral frequency can effectively remove the fundamental frequency component and highlight the fault frequency characteristics. Summary of the Invention

[0004] This invention addresses the aforementioned problems by providing a method, apparatus, and storage medium for AC motor fault diagnosis based on modulated bispectral frequency.

[0005] To achieve the above objectives, the present invention employs the following technical solution:

[0006] In a first aspect, the present invention provides a method for diagnosing AC motor faults based on modulated dual spectrum, comprising the following steps:

[0007] The phase current I0 and phase voltage U0 of the AC motor are acquired in real time, and the phase current I0 and phase voltage U0 are preprocessed to obtain the current signal I1 and the voltage signal U1.

[0008] Perform a short-time Fourier transform on the current signal I1 to obtain the time spectrum diagram TFS1 of the current signal I1, and extract the single-peak ridge line R1 of the time spectrum diagram TFS1.

[0009] Time-frequency analysis based on modulation bispectrum is performed on the current signal I1, and the time spectrum diagram TFS2 of the current signal I1 is obtained based on the single peak ridge line R1. The single peak ridge line R2 of the time spectrum diagram TFS2 is extracted.

[0010] The phase average power PI is obtained based on the current signal I1, the voltage signal U1, and the single-peak ridge line R1.

[0011] The characteristic frequency threshold H1 for determining whether a fault has occurred is determined based on the ratio of the phase average power PI to the rated power PR of the AC motor.

[0012] The fault characteristic frequency amplitude HF is obtained based on the current signal I1 and the single-peak ridge line R2.

[0013] The ratio of the fault characteristic frequency amplitude HF to the characteristic frequency threshold H1 is used to determine whether the AC motor has a broken bar fault and the severity of the fault.

[0014] Furthermore, the step of performing time-frequency analysis based on modulation bispectrum on the current signal I1, obtaining the time-frequency spectrum TFS2 of the current signal I1 based on the single-peak ridge R1, and extracting the single-peak ridge R2 of the time-frequency spectrum TFS2 specifically involves:

[0015] The current signal I1 is divided into X segments according to a preset segmentation method to obtain X segments of current signal IN1;

[0016] Windowing is applied to the X segments of current signal IN1 to obtain X time-domain signals IN2;

[0017] Modulation bispectral analysis was performed on X time-domain signals IN2 to obtain X three-dimensional spectra SD1;

[0018] Based on the midpoint TN2 of X time-domain signals IN2 and the frequency extreme value FN2 corresponding to the single-peak ridge line R1, the corresponding three-dimensional spectrum SD1 is sliced ​​along the frequency extreme value FN2 to obtain X two-dimensional spectrum TDS1.

[0019] Arrange the X two-dimensional spectrum diagrams TDS1 according to the order of their intermediate times to obtain the time spectrum diagram TFS2 of the current signal I1;

[0020] Extract the single-peak ridge R2 from the time-frequency spectrum TFS2 of the current signal I1.

[0021] Furthermore, dividing the current signal I1 into X segments according to a preset segmentation method specifically involves:

[0022] The current signal I1 is segmented into data segments of length N, starting from the first data. The starting data of each segment is spaced M data points apart. When the remaining data length is less than N, the segmentation is stopped, resulting in X segments of current signal.

[0023] Furthermore, the step of obtaining the phase-average power PI based on the current signal I1, the voltage signal U1, and the single-peak ridge R1 specifically involves:

[0024] The time corresponding to a certain point It in the current signal I1 is Tt. The frequency Pt corresponding to Tt is obtained according to the single-peak ridge line R1.

[0025] The phase average power PI is obtained by using the frequency Pt, the current signal I1, and the voltage signal U1.

[0026] Furthermore, the step of determining the characteristic frequency threshold H1 for judging whether a fault has occurred based on the ratio of the phase average power PI and the rated power PR of the AC motor specifically involves:

[0027] The rated power PR is obtained through the AC motor's own parameters. An initial threshold H0 is set. The characteristic frequency threshold H1 for judging whether a fault has occurred is obtained based on the ratio of the phase average power PI to the rated power PR and the initial threshold H0.

[0028] Furthermore, the step of obtaining the fault characteristic frequency amplitude HF based on the current signal I1 and the single-peak ridge line R2 specifically involves:

[0029] The time corresponding to any point It in the current signal I1 is Tt. The fault characteristic frequency amplitude HF corresponding to Tt is obtained according to the single-peak ridge line R2.

[0030] Secondly, the present invention provides an AC motor fault diagnosis device based on modulation dual spectrum, comprising:

[0031] Data acquisition module: used to acquire the phase current I0 and phase voltage U0 of AC motor in real time, and preprocess the phase current I0 and phase voltage U0 to obtain current signal I1 and phase voltage U1;

[0032] Single-peak ridge R1 extraction module: used to perform short-time Fourier transform on the current signal I1 to obtain the time spectrum diagram TFS1 of the current signal I1, and extract the single-peak ridge R1 of the time spectrum diagram TFS1;

[0033] Single-peak ridge R2 extraction module: used to perform time-frequency analysis based on modulation bispectrum on the current signal I1, and obtain the time spectrum diagram TFS2 of the current signal I1 based on the single-peak ridge R1, and extract the single-peak ridge R2 of the time spectrum diagram TFS2;

[0034] Phase average power acquisition module: used to acquire phase average power PI based on the current signal I1, voltage signal U1, and single-peak ridge R1;

[0035] Feature frequency threshold acquisition module: used to determine the feature frequency threshold H1 for judging whether a fault has occurred based on the ratio of the phase average power PI and the rated power PR of the AC motor;

[0036] Characteristic frequency amplitude acquisition module: used to acquire the fault characteristic frequency amplitude HF based on the current signal I1 and the single-peak ridge R2;

[0037] Fault diagnosis module: used to determine whether the AC motor has a broken bar fault and the severity of the fault by the ratio of the fault characteristic frequency amplitude HF to the characteristic frequency threshold H1.

[0038] Thirdly, the present invention provides a storage medium storing a computer program, which, when executed by a main controller, implements each step of the AC motor fault diagnosis method based on modulation bispectrum.

[0039] Compared with the prior art, the present invention has the following advantages:

[0040] This invention acquires current and voltage signals from an AC motor in real time, performs time-frequency analysis on the current signal using short-time Fourier transform, and extracts the single-peak ridge R1 of the time-frequency spectrum TFS1. It then performs time-frequency analysis on the current signal using a modulated bispectral method and extracts the single-peak ridge R2 of the time-frequency spectrum TFS2. The phase-average power PI is obtained based on the current signal, voltage signal, and single-peak ridge R1. The characteristic frequency threshold H1 for determining whether a fault has occurred is determined based on the ratio of the phase-average power PI to the rated power PR of the AC motor. The fault characteristic frequency amplitude HF is obtained based on the current signal I1 and the single-peak ridge R2. The ratio of the fault characteristic frequency amplitude HF to the characteristic frequency threshold H1 is used to determine whether the AC motor has experienced a broken bar fault and the severity of the fault. By applying the modulated bispectral method to motor fault diagnosis, this invention effectively removes the fundamental frequency component, highlights the fault frequency characteristics, and effectively filters out frequency modulation components, which is beneficial for fault characteristic judgment. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating the AC motor fault diagnosis method based on modulation bispectrum.

[0042] Figure 2 This is a schematic diagram of the structure of an AC motor fault diagnosis device based on modulation bispectrum. Detailed Implementation

[0043] To further illustrate the technical solution of the present invention, the present invention will be further described below through embodiments. Example 1

[0044] like Figure 1 As shown, the AC motor fault diagnosis method based on modulation bispectrum in this embodiment includes the following steps:

[0045] S1, Real-time acquisition of phase current I0 and phase voltage U0 of AC motor, preprocessing of phase current I0 and phase voltage U0 to obtain current signal I1 and voltage signal U1;

[0046] S2, perform a short-time Fourier transform on the current signal I1 to obtain the time spectrum diagram TFS1 of the current signal I1, and extract the single-peak ridge line R1 of the time spectrum diagram TFS1.

[0047] S3, perform time-frequency analysis based on modulation bispectrum on the current signal I1, and obtain the time spectrum diagram TFS2 of the current signal I1 based on the single peak ridge line R1, and extract the single peak ridge line R2 of the time spectrum diagram TFS2.

[0048] S4, obtain the phase average power PI based on the current signal I1, the voltage signal U1, and the single-peak ridge line R1;

[0049] S5. Determine the characteristic frequency threshold H1 for judging whether a fault has occurred based on the ratio of the phase average power PI and the rated power PR of the AC motor.

[0050] S6. Obtain the fault characteristic frequency amplitude HF based on the current signal I1 and the single-peak ridge line R2.

[0051] S7. The ratio of the fault characteristic frequency amplitude HF to the characteristic frequency threshold H1 is used to determine whether the AC motor has a broken bar fault and the severity of the fault.

[0052] It is understood that in this embodiment, current and voltage transformers are used to collect the phase current I0 and phase voltage U0 of the AC motor. A Chebyshev low-pass filter is used to filter out the high-frequency components in the phase current I0 and phase voltage U0, and a sliding window method is used to remove abnormal data in the filtered current to obtain the current signal I1 and voltage signal U1.

[0053] Perform a short-time Fourier transform on the current signal I1 to obtain the time spectrum TFS1 ([time, frequency, amplitude]) of the current signal I1, and extract the single-peak ridge R1 ([time, frequency value, maximum amplitude]) of the time spectrum TFS1.

[0054] Time-frequency analysis based on modulation bispectrum is performed on the current signal I1, and the time-frequency spectrum TFS2 of the current signal I1 is obtained based on the single-peak ridge R1. The single-peak ridge R2 of the time-frequency spectrum TFS2 is extracted, specifically as follows:

[0055] The current signal I1 is divided into X segments according to a preset segmentation method. Starting from the first data, the current signal I1 is segmented into data segments of length N, with the starting data segments spaced M apart. When the data length is insufficient to guarantee N, the segmentation is terminated, resulting in X data segments, i.e., X segments of current signal IN1. Simply put, assuming the total length of the current signal I1 is 20 current data points, the length of each data segment is 5, and the starting data interval of each data segment is 2, then data segment one is 1-5, data segment two is 3-7, data segment three is 5-9, and so on.

[0056] Windowing is applied to the X segments of current signal IN1 to obtain X time-domain signals IN2, calculated using the following formula: ; that is, 1-5, data segment 2 is 3-7, data segment 3 is 5-9...

[0057] Windowing is applied to the X segments of current signal IN1 to obtain X time-domain signals IN2, calculated using the following formula: in, ; In the formula, [ n ] represents the index of the current sample. N It is the window length.

[0058] Modulation bispectral analysis is performed on X time-domain signals IN2 respectively to obtain a three-dimensional spectrum SD1 ([frequency, frequency, amplitude]), calculated as follows: ; in, ; In the formula, SD1 ( f 1, f 2) Represents the frequency coordinates ( f 1, f 2) The amplitude corresponding to the position; X ( f 2) Represents the frequency after performing a Fourier transform on the time-domain signal IN2. f The amplitude corresponding to 2; X ( f 2+ f 1) Represents the frequency after performing a Fourier transform on the time-domain signal IN2. f 2+ f The amplitude corresponding to 1; X ( f 2- f 1) Represents the frequency after performing a Fourier transform on the time-domain signal IN2. f 2- f The amplitude corresponding to 1; E {.} represents the expected value of a mathematical expression.

[0059] Based on the midpoint TN2 of each time domain signal IN2 and the frequency extreme value FN2 corresponding to the single-peak ridge line R1 ([time, frequency extreme value, amplitude]), the corresponding three-dimensional spectrum SD1 ([frequency, frequency, amplitude]) is sliced ​​along the frequency extreme value FN2 to obtain X two-dimensional spectrum TDS1 ([frequency, amplitude]).

[0060] Arrange the X two-dimensional spectrum diagrams TDS1 in chronological order of their intermediate moments to obtain the time spectrum diagram TFS2 ([time, frequency, amplitude]) of the current signal I1.

[0061] Extract the single-peak ridge line R2([time, frequency, amplitude]) from the time-frequency spectrum TFS2([time, frequency, amplitude]), which gives the frequency corresponding to the maximum amplitude at each time point.

[0062] The phase-average power PI is obtained based on the current signal I1, the voltage signal U1, and the single-peak ridge R1, specifically as follows:

[0063] The time corresponding to a certain point It in the current signal I1 is Tt. Based on the single-peak ridge line R1 ([time, frequency value, maximum amplitude]), the frequency Pt corresponding to Tt is obtained.

[0064] The phase average power PI is obtained by using the frequency Pt, current signal I1, and voltage signal U1, and the calculation formula is as follows: ; In the formula, .

[0065] The characteristic frequency threshold H1 for determining whether a fault has occurred is determined based on the ratio of the phase average power PI to the rated power PR of the AC motor, specifically as follows:

[0066] The rated power PR is obtained by using the AC motor's own parameters. An initial threshold H0 is set. Based on the ratio of the phase average power PI to the rated power PR and the initial threshold H0, the threshold H1 for determining whether a fault has occurred is obtained. The calculation formula is as follows: ; In the formula, the rated power PR is obtained from the AC motor's own parameters, and H0 is the initial threshold.

[0067] The fault characteristic frequency amplitude HF is obtained based on the current signal I1 and the single-peak ridge line R2, specifically as follows:

[0068] The time corresponding to a certain point It in the current signal I1 is Tt. The fault characteristic frequency amplitude HF corresponding to Tt is obtained according to the single-peak ridge line R2 ([time, frequency value, maximum amplitude]).

[0069] Calculate the ratio of the fault characteristic frequency amplitude HF to the characteristic frequency threshold H1, and determine the severity of the fault according to Table 1.

[0070] Table 1. Fault Severity Table HF to H1 ratio Motor rotor status suggestion ≤0.6 excellent none 0.6~0.85 good none 0.85~1 Risk exists Monitoring changing trends 1~1.75 1-2 broken bars Shorten the monitoring interval and observe the changing trend. ≥1.75 Multiple broken bars Repair or replace as soon as possible . Example 2

[0071] like Figure 2 As shown, the AC motor fault diagnosis device based on modulation bispectrum includes:

[0072] Data acquisition module 1: used to acquire the phase current I0 and phase voltage U0 of the AC motor in real time, and preprocess the phase current I0 and phase voltage U0 to obtain the current signal I1 and voltage signal U1;

[0073] Single-peak ridge R1 extraction module 2: used to perform short-time Fourier transform on the current signal I1 to obtain the time spectrum diagram TFS1 of the current signal I1, and extract the single-peak ridge R1 of the time spectrum diagram TFS1;

[0074] Single-peak ridge line R2 extraction module 3: used to perform time-frequency analysis based on modulation bispectrum on the current signal I1, and obtain the time spectrum diagram TFS2 of the current signal I1 based on the single-peak ridge line R1, and extract the single-peak ridge line R2 of the time spectrum diagram TFS2.

[0075] Phase average power acquisition module 4: used to acquire phase average power PI based on the current signal I1, voltage signal U1, and single-peak ridge R1;

[0076] Feature frequency threshold acquisition module 5: used to determine the feature frequency threshold H1 for judging whether a fault has occurred based on the ratio of the phase average power PI and the rated power PR of the AC motor;

[0077] Characteristic frequency amplitude acquisition module 6: used to acquire the fault characteristic frequency amplitude HF based on the current signal I1 and the single-peak ridge line R2;

[0078] Fault Judgment Module 7: Used to determine whether the AC motor has a broken bar fault and the severity of the fault by the ratio of the fault characteristic frequency amplitude HF to the characteristic frequency threshold H1.

[0079] Example 3

[0080] This embodiment provides a storage medium storing a computer program, which, when executed by a host controller, implements the various steps in the above method.

[0081] It is understood that the storage medium mentioned above can be a read-only memory, a hard disk, or an optical disk, etc.

[0082] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A fault diagnosis method for AC motors based on modulated bispectral modulation, characterized in that, Includes the following steps: The phase current I0 and phase voltage U0 of the AC motor are acquired in real time, and the phase current I0 and phase voltage U0 are preprocessed to obtain the current signal I1 and the voltage signal U1. Perform a short-time Fourier transform on the current signal I1 to obtain the time spectrum diagram TFS1 of the current signal I1, and extract the single-peak ridge line R1 of the time spectrum diagram TFS1. Time-frequency analysis based on modulation bispectrum is performed on the current signal I1, and the time spectrum diagram TFS2 of the current signal I1 is obtained based on the single peak ridge line R1. The single peak ridge line R2 of the time spectrum diagram TFS2 is extracted. The phase average power PI is obtained based on the current signal I1, the voltage signal U1, and the single-peak ridge line R1. The characteristic frequency threshold H1 for determining whether a fault has occurred is determined based on the ratio of the phase average power PI to the rated power PR of the AC motor. The fault characteristic frequency amplitude HF is obtained based on the current signal I1 and the single-peak ridge line R2. The ratio of the fault characteristic frequency amplitude HF to the characteristic frequency threshold H1 is used to determine whether the AC motor has a broken bar fault and the severity of the fault.

2. The AC motor fault diagnosis method based on modulation bispectrum according to claim 1, characterized in that, The step of performing time-frequency analysis based on modulation bispectrum on the current signal I1, obtaining the time-frequency spectrum TFS2 of the current signal I1 based on the single-peak ridge R1, and extracting the single-peak ridge R2 of the time-frequency spectrum TFS2 is specifically as follows: The current signal I1 is divided into X segments according to a preset segmentation method to obtain X segments of current signal IN1; Windowing is applied to the X segments of current signal IN1 to obtain X time-domain signals IN2; Modulation bispectral analysis was performed on X time-domain signals IN2 to obtain X three-dimensional spectra SD1; Based on the midpoint TN2 of X time-domain signals IN2 and the frequency extreme value FN2 corresponding to the single-peak ridge line R1, the corresponding three-dimensional spectrum SD1 is sliced ​​along the frequency extreme value FN2 to obtain X two-dimensional spectrum TDS1. Arrange the X two-dimensional spectrum diagrams TDS1 according to the order of their intermediate times to obtain the time spectrum diagram TFS2 of the current signal I1; Extract the single-peak ridge R2 from the time-frequency spectrum TFS2 of the current signal I1.

3. The AC motor fault diagnosis method based on modulation bispectrum according to claim 2, characterized in that, The process of dividing the current signal I1 into X segments according to a preset segmentation method is as follows: The current signal I1 is segmented into data segments of length N, starting from the first data. The starting data of each segment is spaced M data points apart. When the remaining data length is less than N, the segmentation is stopped, resulting in X segments of current signal.

4. The AC motor fault diagnosis method based on modulation bispectrum according to claim 3, characterized in that, The step of obtaining the phase average power PI based on the current signal I1, the voltage signal U1, and the single-peak ridge R1 is specifically as follows: The time corresponding to a certain point It in the current signal I1 is Tt. The frequency Pt corresponding to Tt is obtained according to the single-peak ridge line R1. The phase average power PI is obtained by using the frequency Pt, the current signal I1, and the voltage signal U1.

5. The AC motor fault diagnosis method based on modulation bispectrum according to claim 4, characterized in that, The characteristic frequency threshold H1 for determining whether a fault has occurred is determined based on the ratio of the phase average power PI to the rated power PR of the AC motor, specifically as follows: The rated power PR is obtained through the AC motor's own parameters. An initial threshold H0 is set. The characteristic frequency threshold H1 for judging whether a fault has occurred is obtained based on the ratio of the phase average power PI to the rated power PR and the initial threshold H0.

6. The AC motor fault diagnosis method based on modulated bispectrum according to claim 5, characterized in that, The process of obtaining the fault characteristic frequency amplitude HF based on the current signal I1 and the single-peak ridge line R2 is specifically as follows: The time corresponding to any point It in the current signal I1 is Tt. The fault characteristic frequency amplitude HF corresponding to Tt is obtained according to the single-peak ridge line R2.

7. A fault diagnosis device for AC motors based on modulated bispectral modulation, characterized in that, include: Data acquisition module: used to acquire the phase current I0 and phase voltage U0 of AC motor in real time, and preprocess the phase current I0 and phase voltage U0 to obtain current signal I1 and phase voltage U1; Single-peak ridge R1 extraction module: used to perform short-time Fourier transform on the current signal I1 to obtain the time spectrum diagram TFS1 of the current signal I1, and extract the single-peak ridge R1 of the time spectrum diagram TFS1; Single-peak ridge R2 extraction module: used to perform time-frequency analysis based on modulation bispectrum on the current signal I1, and obtain the time spectrum diagram TFS2 of the current signal I1 based on the single-peak ridge R1, and extract the single-peak ridge R2 of the time spectrum diagram TFS2; Phase average power acquisition module: used to acquire phase average power PI based on the current signal I1, voltage signal U1, and single-peak ridge R1; Feature frequency threshold acquisition module: used to determine the feature frequency threshold H1 for judging whether a fault has occurred based on the ratio of the phase average power PI and the rated power PR of the AC motor; Characteristic frequency amplitude acquisition module: used to acquire the fault characteristic frequency amplitude HF based on the current signal I1 and the single-peak ridge R2; Fault diagnosis module: used to determine whether the AC motor has a broken bar fault and the severity of the fault by the ratio of the fault characteristic frequency amplitude HF to the characteristic frequency threshold H1.

8. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by the main controller, implements each step of the AC motor fault diagnosis method based on modulation bispectrum as described in any one of claims 1-6.

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