Harmonic detection-based gearbox multi-rotor bearing fault detection method and device

Abstract

The application belongs to the technical field of data processing, and specifically discloses a gear box multi-rotating shaft bearing fault detection method and device based on harmonic detection. According to the application, the extreme value screening is performed on the vibration peak spectrum in the normal state and the vibration peak spectrum in the fault state respectively according to the frequency; the high-precision harmonic detection is performed on the screened vibration peak spectrum, and the fault detection is performed on the bearing to be detected in combination with the bearing theoretical fault characteristic frequency. Through the above-mentioned mode, when there is a local defect in the rolling bearing, a repetitive impact signal is generated, the spectrum characteristics thereof are that there are clear fault characteristic frequencies and harmonic spectrum lines, at this time, the harmonic component must be a local maximum point, by using this attribute, the vibration peak spectrum is determined based on a multi-scale peak value searching algorithm, the extreme value screening is performed based on a frequency slicing algorithm, and the weak signal fault detection is realized in combination with the bearing theoretical fault characteristic frequency, so that the accuracy and efficiency of the fault detection can be effectively improved.

Description

Technical Field

[0001] This application belongs to the field of data processing technology, and more specifically, relates to a fault detection method and apparatus for multi-shaft bearings in gearboxes based on harmonic detection. Background Technology

[0002] As a key component in power transmission, the transmission gearbox operates under highly variable conditions in harsh environments. Its complex internal structure comprises numerous precision components, with gears, shafts, and bearings tightly coupled and influencing each other. This causes bearing fault signals to continuously attenuate along the complex gear and shaft coupling transmission path. By the time the signal reaches the gearbox surface, it is significantly masked by background vibration noise, resulting in a markedly reduced signal-to-noise ratio of the fault characteristic signal. When a bearing experiences a spalling fault, the impact causes high-frequency vibrations in the structure. These high-frequency vibration signals have clear physical meanings and correspond to different fault types; therefore, bearing condition monitoring and fault diagnosis are largely based on high-frequency vibration signals. However, in actual shipboard conditions, due to space constraints, only 2-3 vibration measurement points are typically placed on the gearbox, mostly concentrated on the outer side of the hull. This not only limits the coverage of the measurement points but also exposes the gearbox to numerous vibration interferences (multiple pairs of gears and bearings operating simultaneously, with numerous and complex frequency components), various speed ratios, diverse operating conditions, and weak fault signals. Accurate extraction of bearing fault characteristics is extremely difficult, which limits the establishment of an online automatic high-reliability diagnostic scheme and comprehensive technical diagnostic standards for weak bearing faults in transmission gearboxes.

[0003] Currently, research on ordinary rolling bearings in the field of fault detection technology has become increasingly mature and has good practical engineering application value. However, when traditional common bearing fault diagnosis methods are applied to gearbox rolling bearings, they fail to achieve the expected results. There are two main reasons for this: first, the numerous rotating parts inside the gearbox during operation result in complex frequency components in the collected vibration signals, with gear and shaft vibration signals being prominent and masking the bearing vibration signals; second, the gearbox vibration signals can only be measured from the outer casing, and the complex transmission path introduces strong background noise, further obscuring the originally weak bearing fault signals. Therefore, the accuracy and efficiency of the above methods for fault detection of multi-shaft bearings in gearboxes are relatively low. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this application is to provide a fault detection method and apparatus for multi-shaft bearings in gearboxes based on harmonic detection. This aims to solve the problems of low accuracy and efficiency in fault detection of multi-shaft bearings in gearboxes due to the complex frequency components in the acquired vibration signals, the prominent vibration signals of gears and shafts, the influence of complex transmission paths on the measurement of vibration signals of gearboxes, and the introduction of strong background noise.

[0005] To achieve the above objectives, in a first aspect, this application provides a fault detection method for multi-shaft bearings in gearboxes based on harmonic detection, comprising:

[0006] The acceleration spectrum under normal conditions is determined based on the vibration acceleration waveform of the multi-shaft gearbox system under normal conditions, and the acceleration spectrum under fault conditions is determined based on the vibration acceleration waveform of the outer ring of the bearing to be tested under fault conditions.

[0007] Based on the multi-scale peak finding algorithm, the vibration peak spectrum in the normal state is determined according to the acceleration spectrum in the normal state, and the vibration peak spectrum in the fault state is determined according to the acceleration spectrum in the fault state.

[0008] Based on the frequency slicing algorithm, extreme value screening is performed on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions according to the frequency.

[0009] High-precision harmonic detection is performed on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions after screening. Fault detection is then performed on the bearing to be tested based on the harmonic detection spectrum and the bearing's theoretical fault characteristic frequency.

[0010] In one embodiment, the steps of determining the acceleration spectrum under normal conditions based on the vibration acceleration waveform of the multi-shaft gearbox system under normal conditions, and determining the acceleration spectrum under fault conditions based on the vibration acceleration waveform of the outer ring of the bearing to be detected under fault conditions, include:

[0011] The vibration acceleration waveforms of the multi-shaft gearbox system under normal conditions and the outer ring of the bearing to be tested under fault conditions are obtained respectively. The same parameters are used to truncate the vibration acceleration waveforms under normal conditions and under fault conditions.

[0012] Based on the target window function, the captured vibration acceleration waveforms under normal conditions and under fault conditions are windowed respectively.

[0013] The vibration acceleration waveforms under normal conditions and under fault conditions are converted using the Fourier transform algorithm to obtain the acceleration spectrum under normal conditions and the acceleration spectrum under fault conditions.

[0014] In one embodiment, the steps of determining the vibration peak spectrum in the normal state based on the acceleration spectrum in the normal state and determining the vibration peak spectrum in the fault state based on the acceleration spectrum in the fault state, using a multi-scale peak lookup algorithm, include:

[0015] The local maximum scale spectral matrix of the acceleration spectrum under normal conditions and the local maximum scale spectral matrix of the acceleration spectrum under fault conditions are calculated based on the multi-scale peak finding algorithm.

[0016] The local maximum scale spectral matrix of the acceleration spectrum under normal conditions and the local maximum scale spectral matrix of the acceleration spectrum under fault conditions are summed row by row, and a first target matrix is ​​generated based on the first summation result, and a second target matrix is ​​generated based on the second summation result.

[0017] The standard deviation of each column of the first target matrix and the second target matrix is ​​calculated respectively. The peak value of the acceleration spectrum in the normal state is detected based on the standard deviation of each first target matrix, and the peak value of the acceleration spectrum in the fault state is detected based on the standard deviation of the second target matrix.

[0018] Calculate the relative height difference between the peak value of the acceleration spectrum under normal conditions and the peak value of the acceleration spectrum under fault conditions, respectively;

[0019] The peak coefficient of the maximum point of the acceleration spectrum under normal conditions is calculated based on the relative height difference of the peak values ​​of the acceleration spectrum under normal conditions, and the maximum points of the acceleration spectrum under normal conditions with peak coefficients less than a preset threshold are removed to obtain the vibration peak spectrum under normal conditions.

[0020] The peak coefficient of the maximum point of the acceleration spectrum under the fault state is calculated based on the relative height difference of the peak values ​​of the acceleration spectrum under the fault state, and the maximum points of the acceleration spectrum under the fault state with peak coefficients less than the preset threshold are removed to obtain the vibration peak spectrum under the fault state.

[0021] In one embodiment, the step of calculating the relative height difference between the peak values ​​of the acceleration spectrum under normal conditions and the peak values ​​of the acceleration spectrum under fault conditions includes:

[0022] The peak values ​​of the acceleration spectrum under normal conditions and the peak values ​​of the acceleration spectrum under fault conditions are marked by preset labels respectively.

[0023] Extend a horizontal line to the left and right based on the preset label, and detect the extension result of the horizontal line in real time;

[0024] When the extended result meets the preset conditions, the minimum signal value of the two intervals is obtained;

[0025] The signal reference level is determined based on the minimum signal value of the two intervals, and the relative height difference between the peak value of the acceleration spectrum in the normal state and the peak value of the acceleration spectrum in the fault state is calculated based on the signal reference level.

[0026] In one embodiment, the step of performing extreme value screening on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions based on the frequency slicing algorithm includes:

[0027] Obtain the frequencies of the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions;

[0028] Obtain the rotational frequency and meshing frequency of each shaft in a multi-shaft gearbox system;

[0029] The frequency resolution, the index of the harmonics of the frequency, and the upper and lower ranges of the frequency slice are determined respectively.

[0030] Based on the frequency slicing algorithm, the maximum points of the harmonics of the frequency in the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions are filtered according to the frequency resolution, the index of the harmonics of the frequency, and the upper and lower ranges of the frequency slice.

[0031] In one embodiment, the step of performing high-precision harmonic detection on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions, respectively, and performing fault detection on the bearing to be detected based on the harmonic detection spectrum and the bearing's theoretical fault characteristic frequency, includes:

[0032] Determine the target detection range of the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions after screening;

[0033] Based on the adaptive local search algorithm, high-precision harmonic detection is performed on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions after screening, according to the target detection range.

[0034] Calculate the number of harmonics detected and the sum of harmonic amplitudes based on the harmonic detection spectrum.

[0035] The bearing to be tested for fault is tested based on the number of harmonics detected, the sum of the harmonic amplitudes, and the theoretical fault characteristic frequency of the bearing.

[0036] Secondly, this application provides a fault detection device for multi-shaft bearings in gearboxes based on harmonic detection, comprising:

[0037] The determination module is used to acquire the vibration acceleration waveform of the multi-shaft gearbox system under normal conditions and the vibration acceleration waveform of the outer ring of the bearing to be detected under fault conditions, and to determine the acceleration spectrum under normal conditions based on the vibration acceleration waveform under normal conditions, and to determine the acceleration spectrum under fault conditions based on the vibration acceleration waveform under fault conditions.

[0038] The determining module is further configured to determine the vibration peak spectrum in the normal state based on the acceleration spectrum in the normal state, and to determine the vibration peak spectrum in the fault state based on the acceleration spectrum in the fault state, using a multi-scale peak search algorithm.

[0039] The filtering module is used to perform extreme value filtering on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions based on the frequency slicing algorithm.

[0040] The detection module is used to perform high-precision harmonic detection on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions after screening, and to perform fault detection on the bearing to be detected based on the harmonic detection spectrum and the bearing's theoretical fault characteristic frequency.

[0041] Thirdly, this application provides an electronic device, comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute the method described in the first aspect or any possible implementation thereof.

[0042] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0043] Fifthly, this application provides a computer program product that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0044] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0045] Overall, the technical solutions conceived in this application have the following beneficial effects compared with the prior art:

[0046] (1) When there are local defects in rolling bearings, repetitive impact signals will be generated. The spectral characteristics of these signals are characterized by clear fault characteristic frequencies and their harmonic spectra. At this time, the harmonic components must be local maxima. This application utilizes this property to perform high-precision harmonic detection on the vibration peak spectrum based on an adaptive local search algorithm. Combined with the bearing theoretical fault characteristic frequency, it realizes the fault detection of weak signals. This enables the detection of weak signals of bearing faults in multi-shaft gearbox systems under complex component interference, thereby effectively improving the accuracy and efficiency of fault detection.

[0047] (2) To detect weak amplitudes under noise interference, this application introduces a multi-scale peak lookup algorithm. The peak vibration spectrum under normal conditions is determined based on the acceleration spectrum under normal conditions, and the peak vibration spectrum under fault conditions is determined based on the acceleration spectrum under fault conditions. All maxima and their corresponding positions of the peak vibration spectrum are then obtained. Furthermore, this application proposes a new concept of peak coefficient. After calculating the relative height difference of the peak values ​​in the acceleration spectrum, the peak coefficient of the maxima can be calculated based on the relative height difference of the peak values ​​in the acceleration spectrum. Maxima with peak coefficients less than a preset threshold are removed, thus obtaining the peak vibration spectrum under normal conditions and the peak vibration spectrum under fault conditions. This operation can weaken the impact of noise interference to a certain extent, achieving a noise reduction effect.

[0048] In summary, this application determines the acceleration spectrum under normal conditions based on the vibration acceleration waveform of the multi-shaft gearbox system under normal conditions, and determines the acceleration spectrum under fault conditions based on the vibration acceleration waveform of the outer ring of the bearing to be detected under fault conditions; based on a multi-scale peak lookup algorithm, it determines the vibration peak spectrum under normal conditions based on the acceleration spectrum under normal conditions, and determines the vibration peak spectrum under fault conditions based on the acceleration spectrum under fault conditions; based on a frequency slicing algorithm, it performs extreme value filtering on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions according to frequency; it performs high-precision harmonic detection on the filtered vibration peak spectrum under normal conditions and vibration peak spectrum under fault conditions, and performs fault detection on the bearing to be detected based on the harmonic detection spectrum and the theoretical fault characteristic frequency of the bearing. When a rolling bearing has a local defect, it will generate a repetitive impact signal. Its spectral characteristics are characterized by clear fault characteristic frequencies and their harmonic spectral lines. At this time, the harmonic components must be local maxima. By utilizing this property, the vibration peak spectrum can be determined based on a multi-scale peak search algorithm, extreme value screening can be performed based on a frequency slicing algorithm, and weak signal fault detection can be achieved by combining the bearing theoretical fault characteristic frequencies. This can effectively improve the accuracy and efficiency of fault detection. Attached Figure Description

[0049] Figure 1 This is one of the flowcharts of the fault detection method for multi-shaft bearings in a gearbox based on harmonic detection provided in the embodiments of this application;

[0050] Figure 2 This is a waveform diagram of the vibration acceleration of the multi-shaft gearbox system provided in this application under normal conditions;

[0051] Figure 3 This is a waveform diagram of the vibration acceleration of the outer ring of the bearing to be detected in a fault state, provided in an embodiment of this application.

[0052] Figure 4 This is an acceleration spectrum diagram of the multi-shaft gearbox system provided in this application embodiment under normal conditions;

[0053] Figure 5 This is an acceleration spectrum diagram of the outer ring of the bearing to be detected under fault conditions, provided in an embodiment of this application.

[0054] Figure 6 This is the vibration peak spectrum diagram of the selected gearbox multi-shaft system under normal conditions provided in the embodiments of this application;

[0055] Figure 7 This is a peak vibration spectrum of the outer ring of the bearing to be detected under fault conditions, as provided in the embodiments of this application.

[0056] Figure 8 This is a harmonic detection spectrum of the multi-shaft gearbox system provided in this application embodiment under normal conditions;

[0057] Figure 9 This is a harmonic detection spectrum of the outer ring of the bearing to be tested in a fault state, provided in an embodiment of this application.

[0058] Figure 10 This is the second flowchart of the fault detection method for multi-shaft bearings in a gearbox based on harmonic detection provided in the embodiments of this application;

[0059] Figure 11 This is a schematic diagram of the module structure of the fault detection device for multi-shaft bearings of a gearbox based on harmonic detection provided in the embodiments of this application;

[0060] Figure 12 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0062] In this article, the term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The symbol " / " in this article indicates that the related objects are in an "or" relationship; for example, A / B means A or B.

[0063] The terms "first" and "second," etc., used in the specification and claims herein are used to distinguish different objects, not to describe a specific order of objects. For example, "first response message" and "second response message," etc., are used to distinguish different response messages, not to describe a specific order of response messages.

[0064] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0065] Based on this, the embodiments of this application provide a fault detection method for multi-shaft bearings in gearboxes based on harmonic detection, referring to... Figure 1 , Figure 1 This is one of the flowcharts illustrating a fault detection method for multi-shaft bearings in a gearbox based on harmonic detection, provided in this application. In this embodiment, the fault detection method for multi-shaft bearings in a gearbox based on harmonic detection includes steps S10 to S40:

[0066] Step S10: Determine the acceleration spectrum in the normal state based on the vibration acceleration waveform of the multi-shaft gearbox system in the normal state, and determine the acceleration spectrum in the fault state based on the vibration acceleration waveform of the outer ring of the bearing to be detected in the fault state.

[0067] It should be noted that, in this embodiment, when performing fault detection on the multi-shaft bearing of the gearbox, the vibration acceleration signal of the multi-shaft gearbox system under normal conditions and the vibration acceleration signal of the outer ring of the bearing to be tested under fault conditions are selected. At this time, the vibration acceleration waveform of the multi-shaft gearbox system under normal conditions can be referenced... Figure 2 The vibration acceleration waveform of the outer ring of the bearing to be tested under fault conditions can be referenced. Figure 3 Through the above Figure 2 and Figure 3 It can be seen that the sampling frequency of the vibration acceleration signal is 51200Hz, the sampling time is 2s, and the sampling frequency resolution is 0.5Hz.

[0068] Further, step S10 includes: acquiring the vibration acceleration waveform of the gearbox multi-shaft system in normal condition and the vibration acceleration waveform of the outer ring of the bearing to be detected in fault condition, respectively, and using the same parameters to truncate the vibration acceleration waveform in normal condition and the vibration acceleration waveform in fault condition; windowing the truncated vibration acceleration waveform in normal condition and the vibration acceleration waveform in fault condition based on a target window function; and converting the windowed vibration acceleration waveform in normal condition and the vibration acceleration waveform in fault condition based on a Fourier transform algorithm to obtain the acceleration spectrum in normal condition and the acceleration spectrum in fault condition.

[0069] It should be understood that the "same parameter" refers to the parameter used to simultaneously capture multi-dimensional vibration acceleration waveforms. For the vibration acceleration waveforms of a multi-shaft gearbox system under normal conditions and the vibration acceleration waveforms of the outer ring of the bearing to be tested under fault conditions, the same parameter can be used for waveform capture. This same parameter can be a 2-second length. For vibration acceleration waveforms, it can be obtained through... It means that, among them, N Indicates the signal length.

[0070] It is understandable that the target window function refers to the window function used to window the vibration acceleration waveform. This target window function can be a Gaussian window function, and its parameters are... This approach achieves optimal spectral resolution and the narrowest main lobe width, which is beneficial for highlighting weak bearing fault signals under complex interference conditions. Furthermore, other window functions can also be used, such as rectangular windows, Hanning windows, Hamming windows, and Blackman windows, but their harmonic detection performance is not as good as that of the Gaussian window function. Therefore, this embodiment preferably uses the Gaussian window function. This transforms it into a periodic signal, specifically:

[0071] .

[0072] in, This represents a sequence of vibration acceleration waveforms. This represents the length of the Gaussian window function, and Standard deviation of Gaussian random variable Inversely proportional, its exact correspondence with the standard deviation of the Gaussian probability density function is: σ =( L – 1) / (2 α ).

[0073] It should be noted that after windowing based on the target window function, the vibration acceleration waveform under normal conditions can be converted into an acceleration spectrum under normal conditions using a Fourier transform algorithm. For details, please refer to [reference needed]. Figure 4 Based on the Fourier transform algorithm, the windowed vibration acceleration wave under fault conditions is converted into an acceleration spectrum under fault conditions. For details, please refer to [reference needed]. Figure 5 Through the above Figure 4 and Figure 5 It is known that the spectral composition of a multi-shaft gearbox system is quite rich and complex, including the rotational frequency and harmonics of each gear shaft, the meshing frequency and harmonics of each gear, as well as many unknown frequency components.

[0074] Step S20: Based on the multi-scale peak lookup algorithm, determine the vibration peak spectrum in the normal state according to the acceleration spectrum in the normal state, and determine the vibration peak spectrum in the fault state according to the acceleration spectrum in the fault state.

[0075] It is understandable that after determining the acceleration spectrum of the gearbox multi-shaft system under normal conditions and the acceleration spectrum of the outer ring of the bearing to be detected under fault conditions, the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions can be determined based on the multi-scale peak search algorithm. This multi-scale peak search algorithm can be an adaptive multi-scale-based peak detection (AMPD) algorithm to detect weak amplitudes under noise interference.

[0076] Further, step S20 includes: calculating the local maximum scale spectral matrix of the acceleration spectrum under normal conditions and the local maximum scale spectral matrix of the acceleration spectrum under fault conditions based on a multi-scale peak finding algorithm; performing row-by-row summation on the local maximum scale spectral matrix of the acceleration spectrum under normal conditions and the local maximum scale spectral matrix of the acceleration spectrum under fault conditions, respectively, and generating a first target matrix based on a first summation result, and generating a second target matrix based on a second summation result; calculating the standard deviation of each column of the first target matrix and the second target matrix, respectively, detecting the peak value of the acceleration spectrum under normal conditions based on the standard deviation of each first target matrix, and detecting the peak value of the acceleration spectrum under fault conditions based on the standard deviation of the second target matrix. The peak values ​​of the acceleration spectrum are calculated. The relative height difference between the peak values ​​of the acceleration spectrum under normal conditions and the peak values ​​of the acceleration spectrum under fault conditions is calculated. Based on the relative height difference of the peak values ​​of the acceleration spectrum under normal conditions, the peak coefficient of the maximum point of the acceleration spectrum under normal conditions is calculated, and the maximum points in the acceleration spectrum under normal conditions with peak coefficients less than a preset threshold are removed to obtain the vibration peak spectrum under normal conditions. Similarly, based on the relative height difference of the peak values ​​of the acceleration spectrum under fault conditions, the peak coefficient of the maximum point of the acceleration spectrum under fault conditions is calculated, and the maximum points in the acceleration spectrum under fault conditions with peak coefficients less than the preset threshold are removed to obtain the vibration peak spectrum under fault conditions.

[0077] It is understandable that the acceleration spectrum under normal conditions and the acceleration spectrum under fault conditions can be collectively referred to as the acceleration spectrum. ,in, N This represents the signal length. We can then calculate the local maximum scale (LMS) spectral matrix of the acceleration spectrum under normal conditions and under fault conditions. This LMS can be the Local Maxima Scalogram, and the LMS spectral matrix can be... matrix Specifically, it is expressed as:

[0078]

[0079] .

[0080] in, , , This indicates rounding up, and indicates the first integer. , r represents a random number uniformly distributed in the range [0, 1]. constant factor ( ), Representation matrix The element, when and , give The value is .

[0081] It should be noted that after calculating the local maximum scale spectral matrix of the acceleration spectrum under normal conditions and the local maximum scale spectral matrix of the acceleration spectrum under fault conditions, the local maximum scale spectral matrix of the acceleration spectrum under normal conditions is summed row by row, specifically as follows:

[0082] .

[0083] in, It contains information about the scale-dependent distribution of zero (and also local maxima). , , which represents the scale with the most local extrema.

[0084] It should be understood that after summing row by row the local maximum scale spectral matrix of the acceleration spectrum under normal conditions and the local maximum scale spectral matrix of the acceleration spectrum under fault conditions, the local maximum scale spectral matrix that satisfies... All elements Remove, get one New matrix The target matrix is ​​specifically defined as follows: For the local maximum scale spectral matrix of the acceleration spectrum under normal conditions, a first target matrix is ​​generated; for the local maximum scale spectral matrix of the acceleration spectrum under fault conditions, a second target matrix is ​​generated. After generating the first and second target matrices respectively, the peak value of the acceleration spectrum can be detected by calculating the standard deviation of each target matrix.

[0085] .

[0086] in, The standard deviation of the target matrix is ​​represented in... When the index is 0, it indicates that the position of the column is the index of the peak value. At this time, the peak value of the acceleration spectrum is calculated based on the index of the peak value.

[0087] It is understood that this embodiment introduces the concept of peak coefficient. After calculating the relative height difference of the peaks in the acceleration spectrum, the peak coefficient of the maximum point can be calculated based on the relative height difference of the peaks in the acceleration spectrum. Specifically:

[0088] .

[0089] in, Indicates peak value. This indicates the relative height difference of the peak value.

[0090] It should be noted that after obtaining the peak coefficient in the acceleration spectrum, the peak coefficient... Maximum values ​​less than a preset threshold are removed, i.e. The peak vibration spectrum under normal conditions and the peak vibration spectrum under fault conditions are obtained respectively. This operation can reduce the impact of noise interference to a certain extent and achieve the effect of noise reduction.

[0091] Further, the step of calculating the relative height difference between the peak values ​​of the acceleration spectrum in the normal state and the peak values ​​of the acceleration spectrum in the fault state includes: marking the peak values ​​of the acceleration spectrum in the normal state and the peak values ​​of the acceleration spectrum in the fault state using preset labels; extending a horizontal line to the left and right according to the preset labels, and detecting the extension result of the horizontal line in real time; obtaining the minimum signal value of the two intervals when the extension result meets the preset conditions; determining the signal reference level based on the minimum signal value of the two intervals, and calculating the relative height difference between the peak values ​​of the acceleration spectrum in the normal state and the peak values ​​of the acceleration spectrum in the fault state based on the signal reference level.

[0092] It should be understood that relative height difference refers to the difference between the height of a peak and the height of other peaks. It can be used to measure the prominence of a peak. A shorter, isolated peak may be more prominent than a taller peak that is not significant within the peak range. For peaks in the acceleration spectrum, they can be marked with a preset label. That is, a preset label is placed on the peak, and a horizontal line is extended to the left and right from the preset label until the horizontal line meets a preset condition. This preset condition can be that the line passes through the acceleration spectrum until a higher peak appears, reaching the left or right end of the acceleration spectrum. For the minimum signal value of two intervals, this location point can be a trough or one of the signal endpoints. The larger of the two minimum signal values ​​is taken as the reference level. The height of the peak above this reference level is its relative height difference, that is, the relative height difference between the peak value of the acceleration spectrum under normal conditions and the peak value of the acceleration spectrum under fault conditions are determined respectively.

[0093] Step S30: Based on the frequency slicing algorithm, extreme value screening is performed on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions according to the frequency.

[0094] It should be understood that, in order to eliminate interference from rotational and meshing frequencies, this embodiment introduces a frequency slicing algorithm. This algorithm removes the maximum values ​​of frequencies equal to the rotational and meshing frequencies of each shaft in the gearbox and their harmonics from the vibration peak spectrum under normal conditions and under fault conditions. This achieves extreme value filtering of the vibration peak spectrum. The vibration peak spectrum of the multi-shaft gearbox system under normal conditions after filtering can be referenced... Figure 6 The peak vibration spectrum of the outer ring of the selected bearings under fault conditions can be referenced. Figure 7 .

[0095] Step S40: High-precision harmonic detection is performed on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions after screening, and fault detection is performed on the bearing to be detected based on the harmonic detection spectrum and the bearing theoretical fault characteristic frequency.

[0096] Understandably, to detect weak bearing fault signals in a multi-shaft gearbox system under complex interference, a high-precision harmonic detection of the vibration peak spectrum based on an adaptive local search algorithm is required. After detection, the presence of bearing fault characteristic frequencies can be determined by combining the theoretical bearing fault characteristic frequencies, thus completing the fault detection of the bearing to be detected. The specific theoretical bearing fault characteristic frequencies can be found in Table 1.

[0097] Table 1:

[0098]

[0099] Further, step S40 includes: determining the target detection range of the selected vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions; performing high-precision harmonic detection on the selected vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions according to the target detection range based on an adaptive local search algorithm; calculating the number of harmonic detections and the sum of harmonic amplitudes according to the harmonic detection spectrum; and performing fault detection on the bearing to be detected according to the number of harmonic detections, the sum of harmonic amplitudes, and the theoretical fault characteristic frequency of the bearing.

[0100] It should be noted that the target detection range refers to the specified frequency range for high-precision harmonic detection of the vibration peak spectrum. This target detection range can be 200Hz to 1000Hz. After determining the target detection range, an adaptive local search algorithm can be used to perform high-precision harmonic detection on the selected vibration peak spectra under normal conditions and vibration peak spectra under fault conditions, respectively, according to the target detection range. For example, detecting the 1st to 10th harmonics of each frequency within the target detection range, specifically:

[0101]

[0102]

[0103] .

[0104] in, Indicates the frequency to be detected. Indicates frequency resolution. n , indicating harmonic multiples, Indicates the first The index corresponding to the first harmonic. It is the first The upper and lower ranges of the local harmonic search can be adaptively adjusted according to the harmonic order, enabling high-precision harmonic detection. Indicates the first j Within the target detection range of first harmonics, the maximum harmonic amplitude present in the vibration peak spectrum. This indicates that the maximum harmonic amplitude has been found. Precise indexing in the peak vibration spectrum, if v ≠0, then its The first harmonic exists.

[0105] It should be understood that after high-precision harmonic detection, the number of harmonics detected can be calculated based on the harmonic detection spectrum. Sum of harmonic amplitude values Specifically:

[0106]

[0107] .

[0108] in, Indicates the first j Within the target detection range of the first harmonic, the maximum harmonic amplitude present in the peak vibration spectrum.

[0109] It should be noted that when calculating the number of harmonic detectors... Then, the number of harmonics can be determined. Less than the threshold frequency The sum of harmonic amplitudes is set to zero, i.e. This operation can further reduce the impact of noise interference. (Using a threshold) Taking a harmonic count of 5 as an example, the amplitude of frequencies with fewer than 5 harmonics is set to zero to obtain the harmonic detection spectrum. The harmonic detection spectrum of a multi-shaft gearbox system under normal conditions can be referenced. Figure 8 The harmonic detection spectrum of the outer ring of the bearing to be tested under fault conditions can be referenced. Figure 9 By comparison, it can be seen that in Figure 9 There is a distinct frequency of 323.875Hz, which is close to the characteristic frequency of bearing outer ring fault of 329.55Hz. This indicates that the technical solution of this embodiment can realize the detection of weak bearing fault signals in a multi-shaft gearbox system under complex interference.

[0110] This embodiment determines the acceleration spectrum under normal conditions based on the vibration acceleration waveform of the multi-shaft gearbox system under normal conditions, and determines the acceleration spectrum under fault conditions based on the vibration acceleration waveform of the outer ring of the bearing to be detected under fault conditions. Based on a multi-scale peak lookup algorithm, the vibration peak spectrum under normal conditions is determined based on the acceleration spectrum under normal conditions, and the vibration peak spectrum under fault conditions is determined based on the acceleration spectrum under fault conditions. Based on a frequency slicing algorithm, extreme value filtering is performed on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions according to frequency. High-precision harmonic detection is performed on the filtered vibration peak spectra under normal conditions and vibration peak spectra under fault conditions, and fault detection is performed on the bearing to be detected based on the harmonic detection spectrum and the theoretical fault characteristic frequency of the bearing. When a rolling bearing has a local defect, it will generate a repetitive impact signal. Its spectral characteristics are characterized by clear fault characteristic frequencies and their harmonic spectral lines. At this time, the harmonic components must be local maxima. By utilizing this property, the vibration peak spectrum can be determined based on a multi-scale peak search algorithm, extreme value screening can be performed based on a frequency slicing algorithm, and weak signal fault detection can be achieved by combining the bearing theoretical fault characteristic frequencies. This can effectively improve the accuracy and efficiency of fault detection.

[0111] In one specific embodiment, this application provides steps for extreme value screening of the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions, respectively. Please refer to... Figure 10 , Figure 10 This is the second flowchart illustrating the fault detection method for multi-shaft bearings in a gearbox based on harmonic detection provided in this application. Step S30 includes steps S301 to S304:

[0112] Step S301: Obtain the frequency of the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions.

[0113] It should be noted that, in order to eliminate the interference of rotational frequency and meshing frequency, this embodiment needs to use a frequency slicing algorithm to remove the maximum points in the vibration peak spectrum under normal conditions and under fault conditions where the frequency is equal to the rotational frequency and meshing frequency of each shaft of the gearbox and their harmonics. For this purpose, it is necessary to obtain the frequency of the vibration peak spectrum under normal conditions and the frequency of the vibration peak spectrum under fault conditions respectively.

[0114] Step S302: Obtain the rotational frequency and meshing frequency of each shaft in the multi-shaft gearbox system.

[0115] Step S303: Determine the frequency resolution, the index of the harmonics of the frequency, and the upper and lower ranges of the frequency slice.

[0116] It should be understood that in order to effectively improve the accuracy of selecting the maximum points of harmonics, it is necessary to determine the relevant parameters, including but not limited to frequency resolution, harmonic index of the frequency, and upper and lower range of frequency slices.

[0117] Step S304: Based on the frequency slicing algorithm, the maximum points of the harmonics of the vibration peak spectrum in the normal state and the vibration peak spectrum in the fault state are filtered according to the frequency resolution, the index of the harmonics of the frequency, and the upper and lower ranges of the frequency slice.

[0118] Understandably, after determining the frequency resolution, the harmonic indexes, and the upper and lower ranges of the frequency slice, the frequency slicing algorithm can be used to remove the maxima of harmonics in the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions whose frequencies equal the rotational frequency and meshing frequency. Specifically:

[0119] .

[0120] in, Indicates the frequency to be removed. n It is an integer multiple of it. This indicates rounding to the nearest integer. Indicates frequency resolution; This indicates the index of its corresponding harmonic. m This indicates the upper and lower ranges of the frequency slice.

[0121] This embodiment obtains the frequencies of the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions; obtains the rotational frequency and meshing frequency of each shaft in the multi-shaft gearbox system; determines the frequency resolution, the harmonic index of the frequency, and the upper and lower ranges of the frequency slice; based on the frequency slicing algorithm, filters the maxima of harmonics whose frequencies are equal to the rotational frequency and the meshing frequency in the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions according to the frequency resolution, the harmonic index of the frequency, and the upper and lower ranges of the frequency slice. By obtaining the frequencies of the vibration peak spectrum and the rotational frequency and meshing frequency of each shaft in the multi-shaft gearbox system, and introducing a frequency slicing algorithm to filter the maxima of harmonics whose frequencies are equal to the rotational frequency and the meshing frequency according to the frequency resolution, the harmonic index of the frequency, and the upper and lower ranges of the frequency slice, interference from the rotational frequency and the meshing frequency can be effectively eliminated, thereby effectively improving the accuracy of filtering the maxima of harmonics.

[0122] The following describes the fault detection device for multi-shaft bearings in gearboxes based on harmonic detection provided in this application. The fault detection device for multi-shaft bearings in gearboxes based on harmonic detection described below can be referred to in conjunction with the fault detection method for multi-shaft bearings in gearboxes based on harmonic detection described above. Please refer to... Figure 11 , Figure 11 This is a schematic diagram of the module structure of the fault detection device for multi-shaft bearings of a gearbox based on harmonic detection provided in this application embodiment, including:

[0123] The determination module T10 is used to acquire the vibration acceleration waveform of the gearbox multi-shaft system under normal conditions and the vibration acceleration waveform of the outer ring of the bearing to be detected under fault conditions, and to determine the acceleration spectrum under normal conditions based on the vibration acceleration waveform under normal conditions, and to determine the acceleration spectrum under fault conditions based on the vibration acceleration waveform under fault conditions.

[0124] The determining module T10 is further configured to determine the vibration peak spectrum in the normal state based on the acceleration spectrum in the normal state, and to determine the vibration peak spectrum in the fault state based on the acceleration spectrum in the fault state, using a multi-scale peak search algorithm.

[0125] The filtering module T20 is used to perform extreme value filtering on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions based on the frequency slicing algorithm.

[0126] The detection module T30 is used to perform high-precision harmonic detection on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions after screening, and to perform fault detection on the bearing to be detected based on the harmonic detection spectrum and the bearing's theoretical fault characteristic frequency.

[0127] This embodiment determines the acceleration spectrum under normal conditions based on the vibration acceleration waveform of the multi-shaft gearbox system under normal conditions, and determines the acceleration spectrum under fault conditions based on the vibration acceleration waveform of the outer ring of the bearing to be detected under fault conditions. Based on a multi-scale peak lookup algorithm, the vibration peak spectrum under normal conditions is determined based on the acceleration spectrum under normal conditions, and the vibration peak spectrum under fault conditions is determined based on the acceleration spectrum under fault conditions. Based on a frequency slicing algorithm, extreme value filtering is performed on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions according to frequency. High-precision harmonic detection is performed on the filtered vibration peak spectra under normal conditions and vibration peak spectra under fault conditions, and fault detection is performed on the bearing to be detected based on the harmonic detection spectrum and the theoretical fault characteristic frequency of the bearing. When a rolling bearing has a local defect, it will generate a repetitive impact signal. Its spectral characteristics are characterized by clear fault characteristic frequencies and their harmonic spectral lines. At this time, the harmonic components must be local maxima. By utilizing this property, the vibration peak spectrum can be determined based on a multi-scale peak search algorithm, extreme value screening can be performed based on a frequency slicing algorithm, and weak signal fault detection can be achieved by combining the bearing theoretical fault characteristic frequencies. This can effectively improve the accuracy and efficiency of fault detection.

[0128] It is understood that the detailed functional implementation of each of the above modules can be found in the description of the aforementioned method embodiments, and will not be repeated here.

[0129] It should be understood that the above-described device is used to execute the methods in the above embodiments. The implementation principle and technical effect of the corresponding program modules in the device are similar to those described in the above methods. The working process of the device can be referred to the corresponding process in the above methods, and will not be repeated here.

[0130] Based on the methods in the above embodiments, this application provides an electronic device, please refer to... Figure 12 , Figure 12 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application.

[0131] It should be noted that the system may include: a processor 10, a communications interface 20, a memory 30, and a communication bus 40. The processor 10, communications interface 20, and memory 30 communicate with each other via the communication bus 40. The processor 10 can invoke logical instructions stored in the memory 30 to execute the methods described in the above embodiments.

[0132] Furthermore, the logical instructions in the aforementioned memory 30 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0133] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0134] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0135] It is understood that the processor in the embodiments of this application can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, field-programmable gate arrays, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0136] The method steps in this application embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, registers, hard disks, portable hard disks, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor.

[0137] It is understood that the various numerical designations used in the embodiments of this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application. Those skilled in the art will readily understand that the above descriptions are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A fault detection method for multi-shaft bearings in gearboxes based on harmonic detection, characterized in that, include: The acceleration spectrum under normal conditions is determined based on the vibration acceleration waveform of the multi-shaft gearbox system under normal conditions, and the acceleration spectrum under fault conditions is determined based on the vibration acceleration waveform of the outer ring of the bearing to be tested under fault conditions. Based on the multi-scale peak finding algorithm, the vibration peak spectrum in the normal state is determined according to the acceleration spectrum in the normal state, and the vibration peak spectrum in the fault state is determined according to the acceleration spectrum in the fault state. Based on the frequency slicing algorithm, extreme value screening is performed on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions according to the frequency. High-precision harmonic detection is performed on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions after screening, and the bearing to be fault detected is then detected based on the harmonic detection spectrum and the bearing's theoretical fault characteristic frequency. The steps of determining the vibration peak spectrum in the normal state based on the acceleration spectrum in the normal state and determining the vibration peak spectrum in the fault state based on the acceleration spectrum in the fault state, using the multi-scale peak finding algorithm, include: The local maximum scale spectral matrix of the acceleration spectrum under normal conditions and the local maximum scale spectral matrix of the acceleration spectrum under fault conditions are calculated based on the multi-scale peak finding algorithm. The local maximum scale spectral matrix of the acceleration spectrum under normal conditions and the local maximum scale spectral matrix of the acceleration spectrum under fault conditions are summed row by row, and a first target matrix is ​​generated based on the first summation result, and a second target matrix is ​​generated based on the second summation result. The standard deviation of each column of the first target matrix and the second target matrix is ​​calculated respectively. The peak value of the acceleration spectrum in the normal state is detected based on the standard deviation of each first target matrix, and the peak value of the acceleration spectrum in the fault state is detected based on the standard deviation of the second target matrix. Calculate the relative height difference between the peak value of the acceleration spectrum under normal conditions and the peak value of the acceleration spectrum under fault conditions, respectively; The peak coefficient of the maximum point of the acceleration spectrum under normal conditions is calculated based on the relative height difference of the peak values ​​of the acceleration spectrum under normal conditions, and the maximum points of the acceleration spectrum under normal conditions with peak coefficients less than a preset threshold are removed to obtain the vibration peak spectrum under normal conditions. The peak coefficient of the maximum point of the acceleration spectrum under the fault state is calculated based on the relative height difference of the peak values ​​of the acceleration spectrum under the fault state, and the maximum points of the acceleration spectrum under the fault state with peak coefficients less than the preset threshold are removed to obtain the vibration peak spectrum under the fault state.

2. The method as described in claim 1, characterized in that, The steps of determining the acceleration spectrum under normal conditions based on the vibration acceleration waveform of the multi-shaft gearbox system under normal conditions, and determining the acceleration spectrum under fault conditions based on the vibration acceleration waveform of the outer ring of the bearing to be tested under fault conditions, include: The vibration acceleration waveforms of the multi-shaft gearbox system under normal conditions and the outer ring of the bearing to be tested under fault conditions are obtained respectively. The same parameters are used to truncate the vibration acceleration waveforms under normal conditions and under fault conditions. Based on the target window function, the captured vibration acceleration waveforms under normal conditions and under fault conditions are windowed respectively. The vibration acceleration waveforms under normal conditions and under fault conditions are converted using the Fourier transform algorithm to obtain the acceleration spectrum under normal conditions and the acceleration spectrum under fault conditions.

3. The method as described in claim 1, characterized in that, The step of calculating the relative height difference between the peak values ​​of the acceleration spectrum under normal conditions and the peak values ​​of the acceleration spectrum under fault conditions includes: The peak values ​​of the acceleration spectrum under normal conditions and the peak values ​​of the acceleration spectrum under fault conditions are marked by preset labels respectively. Extend a horizontal line to the left and right based on the preset label, and detect the extension result of the horizontal line in real time; When the extended result meets the preset conditions, the minimum signal value of the two intervals is obtained; The signal reference level is determined based on the minimum signal value of the two intervals, and the relative height difference between the peak value of the acceleration spectrum in the normal state and the peak value of the acceleration spectrum in the fault state is calculated based on the signal reference level.

4. The method as described in claim 1, characterized in that, The step of performing extreme value screening on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions based on the frequency slicing algorithm includes: Obtain the frequencies of the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions; Obtain the rotational frequency and meshing frequency of each shaft in a multi-shaft gearbox system; The frequency resolution, the index of the harmonics of the frequency, and the upper and lower ranges of the frequency slice are determined respectively. Based on the frequency slicing algorithm, the maximum points of the harmonics of the frequency in the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions are filtered according to the frequency resolution, the index of the harmonics of the frequency, and the upper and lower ranges of the frequency slice.

5. The method according to any one of claims 1 to 4, characterized in that, The steps of performing high-precision harmonic detection on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions after screening, and then performing fault detection on the bearing to be detected based on the harmonic detection spectrum and the theoretical fault characteristic frequency of the bearing, include: Determine the target detection range of the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions after screening; Based on the adaptive local search algorithm, high-precision harmonic detection is performed on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions after screening, according to the target detection range. Calculate the number of harmonics detected and the sum of harmonic amplitudes based on the harmonic detection spectrum. The bearing to be tested for fault is tested based on the number of harmonics detected, the sum of the harmonic amplitudes, and the theoretical fault characteristic frequency of the bearing.

6. A fault detection device for multi-shaft bearings in gearboxes based on harmonic detection, characterized in that, include: The determination module is used to acquire the vibration acceleration waveform of the multi-shaft gearbox system under normal conditions and the vibration acceleration waveform of the outer ring of the bearing to be detected under fault conditions, and to determine the acceleration spectrum under normal conditions based on the vibration acceleration waveform under normal conditions, and to determine the acceleration spectrum under fault conditions based on the vibration acceleration waveform under fault conditions. The determining module is further configured to determine the vibration peak spectrum in the normal state based on the acceleration spectrum in the normal state, and to determine the vibration peak spectrum in the fault state based on the acceleration spectrum in the fault state, using a multi-scale peak search algorithm. The filtering module is used to perform extreme value filtering on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions based on the frequency slicing algorithm. The detection module is used to perform high-precision harmonic detection on the vibration peak spectrum under normal conditions and the vibration peak spectrum under fault conditions after screening, and to perform fault detection on the bearing to be detected based on the harmonic detection spectrum and the bearing's theoretical fault characteristic frequency. The determining module is further configured to calculate the local maximum scale spectrum matrix of the acceleration spectrum under normal conditions and the local maximum scale spectrum matrix of the acceleration spectrum under fault conditions based on a multi-scale peak finding algorithm; perform row-by-row summation on the local maximum scale spectrum matrix of the acceleration spectrum under normal conditions and the local maximum scale spectrum matrix of the acceleration spectrum under fault conditions respectively, and generate a first target matrix based on the first summation result, and generate a second target matrix based on the second summation result; Calculate the standard deviation of each column of the first target matrix and the second target matrix respectively. Detect the peak value of the acceleration spectrum under normal conditions based on the standard deviation of each of the first target matrices, and detect the peak value of the acceleration spectrum under fault conditions based on the standard deviation of the second target matrix. Calculate the relative height difference between the peak values ​​of the acceleration spectrum under normal conditions and the peak values ​​of the acceleration spectrum under fault conditions. Calculate the peak coefficient of the maximum point of the acceleration spectrum under normal conditions based on the relative height difference of the peak values ​​of the acceleration spectrum under normal conditions, and remove the maximum points of the acceleration spectrum under normal conditions whose peak coefficients are less than a preset threshold to obtain the vibration peak spectrum under normal conditions. Calculate the peak coefficient of the maximum point of the acceleration spectrum under fault conditions based on the relative height difference of the peak values ​​of the acceleration spectrum under fault conditions, and remove the maximum points of the acceleration spectrum under fault conditions whose peak coefficients are less than the preset threshold to obtain the vibration peak spectrum under fault conditions.

7. An electronic device, characterized in that, include: At least one memory for storing computer programs; At least one processor is configured to execute a program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to perform the method as described in any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is run on the processor, it causes the processor to perform the method as described in any one of claims 1-5.

9. A computer program product, characterized in that, When the computer program product is run on a processor, the processor causes the processor to perform the method as described in any one of claims 1-5.

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