CN116519301A - Bearing fault diagnosis method and system based on time-frequency envelope spectrum peak analysis - Google Patents

Abstract

The invention discloses a bearing fault diagnosis method and system based on time-frequency envelope spectrum peak analysis, and relates to the technical field of time-frequency analysis of non-stationary rotating machinery fault signals. The method comprises the steps of obtaining a time spectrum of a vibration signal to be diagnosed, and preprocessing the time spectrum to obtain a preprocessed envelope; constructing a rapid time-frequency envelope spectrum peak value diagram, and decomposing the preprocessed envelope by using the rapid time-frequency envelope spectrum peak value diagram; extracting the frequency point with the most prominent pulse characteristic in the rapid time-frequency envelope spectrum peak value diagram to obtain a time-domain envelope spectrum peak value; and calculating a short-time Fourier transform result of a frequency point corresponding to the time domain envelope spectrum peak value, namely, a fault characteristic frequency, and diagnosing the bearing fault type through the fault characteristic frequency. The invention can detect periodic pulse information in complex signals, inhibit the influence of random pulse, harmonic wave and other interference, extract the fault characteristic frequency of the bearing more accurately and diagnose the health condition of the machinery effectively.

Description

A Bearing Fault Diagnosis Method and System Based on Time-Frequency Envelope Spectrum Peak Analysis

technical field

The invention relates to the technical field of time-frequency analysis of non-stationary rotating machinery fault signals, in particular to a bearing fault diagnosis method and system based on time-frequency envelope spectrum peak analysis.

Background technique

The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

As a basic signal, periodic pulse signal plays a very important role in many fields. Taking the field of signal processing as an example, the fault type of the bearing is judged by analyzing the periodic pulse signal generated by the damaged bearing of the mechanical equipment. However, vibration signals of faulty bearings in rotating machinery often contain multiple components, and it is difficult to predict which types of signals should be included. Periodic pulses, sporadic pulses, harmonics and non-Gaussian noises containing fault information may be generated during the operation of the equipment. How to accurately distinguish various components in the complex vibration signal, extract the effective and sensitive signal characteristics of the fault, and then accurately diagnose the bearing fault is the key to the analysis of the current bearing fault factors. A key criterion for an effective bearing fault diagnosis program is the ability to detect information about defects at an early stage, even in the presence of noisy machine operation.

Narrowband demodulation of the vibration signal makes it possible to extract the part of the rotating machinery that carries fault information. However, the quality of the demodulated signal depends on the frequency band selected for demodulation. Spectral kurtosis is currently a very effective method and is generally considered a powerful tool for detecting pulses. Due to the great efforts of Antoni, spectral kurtosis has been recognized as a milestone in characterizing non-stationary signals, especially bearing fault signals. Some fault diagnosis methods determine whether there is a periodic pulse component in the signal by obtaining the kurtosis index of the original signal and comparing the kurtosis value. However, spectral kurtosis cannot accurately detect periodic pulses in the presence of random noise and single pulses. One of the most serious limitations of spectral kurtosis is that it cannot identify whether a series of pulses are repeated, and because the signal-to-noise ratio is uncontrollable, the periodic pulse components carrying fault information are often hidden in the noise, making it difficult to effectively separate them. In fact, the kurtosis value decreases with increasing pulse repetition rate. In addition, spectral kurtosis is sensitive to noise. And due to the limitations of its theoretical basis assumptions, the spectral kurtosis technique is not suitable for signals obtained from experiments with variable speeds of machines.

Therefore, how to quickly detect periodic pulse information in complex signals in the process of bearing fault diagnosis, and at the same time suppress the influence of random pulses, harmonics and other interferences has become an urgent problem to be solved.

Contents of the invention

In view of the deficiencies in the prior art, the purpose of the present invention is to provide a bearing fault diagnosis method and system based on time-frequency envelope spectrum peak analysis, using the fast time-frequency envelope spectrum peak map to obtain more periodic pulse information, which can Detect periodic pulse information in complex signals, and at the same time suppress the influence of random pulses, harmonics and other interference. In the noisy working environment of rotating machinery, the fault characteristic frequency of bearings can be extracted more accurately, and the health status of the machinery can be effectively diagnosed.

In order to achieve the above object, the present invention is achieved through the following technical solutions:

The first aspect of the present invention provides a bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis, comprising the following steps:

Obtain the time spectrum of the vibration signal to be diagnosed, preprocess the time spectrum, and obtain the preprocessed envelope;

Construct a fast time-frequency envelope spectrum peak map, and use the fast time-frequency envelope spectrum peak map to decompose the preprocessed envelope;

Extract the fault characteristic frequency in the peak graph of the fast time-frequency envelope spectrum, and diagnose the bearing fault type through the fault characteristic frequency;

Among them, the specific steps of extracting the fault characteristic frequency in the fast time-frequency envelope spectrum peak map include: extracting the frequency point with the most prominent pulse feature in the fast time-frequency envelope spectrum peak map to obtain the peak value of the time domain envelope spectrum; The short-time Fourier transform result of the frequency point corresponding to the peak value of the envelope spectrum is the fault characteristic frequency.

Further, preprocessing the time-frequency spectrum is to perform de-average processing on each frequency point in the time-frequency spectrum of the signal.

Further, the specific process of extracting the frequency point with the most prominent pulse feature in the envelope and obtaining the peak value of the envelope spectrum in the time domain is as follows:

Calculate the envelope spectrum value of each frequency point of the short-time Fourier transform;

The frequency point with the most prominent pulse characteristics in the fault signal is represented by the maximum value, which is the peak value of the time-domain envelope spectrum.

Furthermore, the formula for calculating the peak value of the time-domain envelope spectrum is:

Among them, TFES(ω) is the envelope spectrum value of the frequency point, ω is the frequency point, N _w is the window length of the window function, r is the current window length, G _STFT (i,ω _k ) is the short-time Fourier transform function, φ(ω) represents the average value of the short-time Fourier transform result at the frequency point ω, ω _k is the discrete frequency, F _s is the sampling frequency, and i is the corresponding time point under the current window length.

Furthermore, the calculation formula of the short-time Fourier transform function G _STFT (i,ω _k ) is:

Among them, ω _k is the discrete frequency, g[r] is the window function, r is the current window length, N _w is the window length of the window function, F _s is the sampling frequency, i is the corresponding time point under the current window length, ω _k is the discrete frequency, x is the vibration signal, and L is the time shift between consecutive windows.

Further, the first level level0 of the fast time-frequency envelope spectrum peak map is the original signal to be analyzed. The second level level1 decomposes the signal into a binary tree structure, the third level1.6 decomposes the signal into a 1/3 tree structure; the fourth level2 decomposes the signal into a binary tree structure, and the fifth level2.6 decomposes the signal into The ternary tree structure, and so on for the rest of the layers.

Further, the accuracy of periodic pulse signal identification is verified by using the error rate between the center frequency of the periodic pulse signal located at the peak of the time-frequency envelope spectrum and the inherent center frequency of the periodic pulse signal.

The second aspect of the present invention provides a bearing fault diagnosis system based on time-frequency envelope spectrum peak analysis, including:

The signal acquisition module is configured to acquire the time-frequency spectrum of the vibration signal to be diagnosed, preprocess the time-frequency spectrum, and obtain the preprocessed envelope;

The fast time-frequency envelope spectrum peak map module is configured to construct a fast time-frequency envelope spectrum peak map, and utilizes the fast time-frequency envelope spectrum peak map to decompose the preprocessed envelope;

The fault diagnosis module is configured to extract the fault characteristic frequency in the envelope, and diagnose the bearing fault type through the fault characteristic frequency;

Wherein, the fault diagnosis module includes an envelope analysis module, which is configured to extract the fault characteristic frequency in the fast time-frequency envelope spectrum peak diagram. The specific steps include: extracting the frequency point with the most prominent pulse feature in the fast time-frequency envelope spectrum peak diagram. , to obtain the peak value of the time-domain envelope spectrum; calculate the short-time Fourier transform result of the frequency point corresponding to the peak value of the time-domain envelope spectrum, which is the fault characteristic frequency.

The third aspect of the present invention provides a medium on which a program is stored, and when the program is executed by a processor, the steps in the bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis as described in the first aspect of the present invention are implemented .

The fourth aspect of the present invention provides a device, including a memory, a processor, and a program stored on the memory and operable on the processor, when the processor executes the program, the method described in the first aspect of the present invention is implemented Steps in a Bearing Fault Diagnosis Method Based on Time-Frequency Envelope Spectrum Peak Analysis.

The above one or more technical solutions have the following beneficial effects:

The invention discloses a bearing fault diagnosis method and system based on time-frequency envelope spectrum peak analysis. Aiming at the shortcoming that the spectrum kurtosis cannot accurately detect periodic pulses, the invention defines the time-frequency envelope spectrum peak value, and constructs Fast Time-Frequency Envelope Spectrum Peak Map quickly identifies the center frequency of periodic pulse signals. The method of the invention can detect periodic pulse information in complex signals, suppress the influence of random pulses, harmonics and other interferences, realize efficient extraction of fault characteristic frequencies, and finally obtain more accurate mechanical bearing fault diagnosis results.

Advantages of additional aspects of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

Description of drawings

The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention, and the schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations to the present invention.

1 is a flowchart of a bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis in Embodiment 1 of the present invention;

2 is a schematic diagram of the time-domain waveform, short-time Fourier transform results and spectral kurtosis results of the composite signal in Embodiment 1 of the present invention when the signal-to-noise ratio is 5 dB;

3 is a schematic diagram of time-domain waveforms, short-time Fourier transform results and spectral kurtosis results of the composite signal in Embodiment 1 of the present invention when the signal-to-noise ratio is 0 dB;

4 is a schematic diagram of time-domain waveforms, short-time Fourier transform results, and spectral kurtosis results of the composite signal in Embodiment 1 of the present invention when the signal-to-noise ratio is -5 dB;

Fig. 5 is the time-frequency envelope diagram of the short-time Fourier transform result at the frequency f=200Hz in Embodiment 1 of the present invention;

6 is a schematic diagram of the time-domain waveform, short-time Fourier transform result and time-frequency envelope spectrum peak result of the composite signal in Embodiment 1 of the present invention when the signal-to-noise ratio is 5 dB;

7 is a schematic diagram of time-domain waveforms, short-time Fourier transform results, and time-frequency envelope spectrum peak results of the composite signal in Embodiment 1 of the present invention when the signal-to-noise ratio is 0 dB;

8 is a schematic diagram of the time-domain waveform, short-time Fourier transform result and time-frequency envelope spectrum peak result of the composite signal in Embodiment 1 of the present invention when the signal-to-noise ratio is -5 dB;

9 is a schematic diagram of the error rate of the center frequency of the periodic pulse component in the composite signal of time-frequency envelope spectrum peak positioning under different signal-to-noise ratios in Embodiment 1 of the present invention;

Fig. 10 is a schematic diagram of sinusoidal signals, sporadic pulses, periodic pulse signals, Gaussian white noise, waveforms of mixed signals and short-time Fourier transform results of mixed signals in Embodiment 1 of the present invention;

Fig. 11 is a decomposition structure diagram of the fast time-frequency envelope spectrum peak diagram in Embodiment 1 of the present invention;

Fig. 12 is the result of fast time-frequency envelope spectrum peak map processing signal S(t), the time-domain waveform of the filtered signal and the square envelope spectrum of the filtered signal in Embodiment 1 of the present invention;

Fig. 13 is the result of fast spectral kurtosis map processing signal S(t), the time domain waveform of the filtered signal and the square envelope spectrogram of the filtered signal in Embodiment 1 of the present invention;

Fig. 14 is the time-domain waveform of the filtered signal in the second possible frequency band of the fast spectral kurtosis diagram in Embodiment 1 of the present invention and the square envelope spectrum diagram of the filtered signal;

Fig. 15 is the waveform of the vibration signal of the outer ring of the bearing collected under the variable speed working condition in Embodiment 1 of the present invention and its short-time Fourier transform result;

Fig. 16 is a schematic diagram of the results of fast time-frequency envelope spectrum peak map processing of vibration signals, the time domain waveform of the filtered signal and the short-time Fourier transform result of the filtered signal square envelope spectrum in Embodiment 1 of the present invention;

Fig. 17 is a schematic diagram of the result of fast spectral kurtosis processing vibration signal, the time domain waveform of the filtered signal and the short-time Fourier transform result of the squared envelope spectrum of the filtered signal in the first embodiment of the present invention.

Detailed ways

It should be noted that the following detailed description is exemplary and intended to provide further explanation of the present invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

It should be noted that the terminology used here is only for describing specific embodiments, and is not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and/or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and/or combinations thereof;

Embodiment one:

Embodiment 1 of the present invention provides a bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis, as shown in FIG. 1 , including the following steps:

Step 1: Obtain the time-frequency spectrum of the vibration signal to be diagnosed, perform preprocessing on the time-frequency spectrum, and obtain the preprocessed envelope.

Step 1.1, preprocessing the time-frequency spectrum is to perform mean value processing on each frequency point in the time-frequency spectrum of the signal, and then obtain the processed envelope.

In step 1.2, the vibration signals collected from the bearings of rotating machinery often contain various information such as fault information, equipment operation sound, and environmental noise. The vibration characteristics of rolling bearings with local defects can be represented by the amplitude modulation process. The vibration signal x(t) is thus modeled as:

Among them, A _k is the amplitude of the kth fault pulse, 2K is the number of pulses, v(t) is the unit step function, the time period corresponding to the fault characteristic frequency is T ₀ , η is the structural damping characteristic coefficient, ω ₀ corresponds to Based on the resonance frequency excited by the bearing, t _i is the ith uniformly distributed random variable that achieves zero mean, and its standard deviation is within the range of 0.02T0. , n(t) is the sum of random noise, harmonics and other disturbances from the surrounding environment. The role of random variables in the fault diagnosis of rotating machinery bearings is to model and analyze the random characteristics of vibration signals, sensor data and other related parameters, so as to help judge whether there is a fault in the bearing and provide a basis for diagnosis of the fault type.

Step 2, extract the fault characteristic frequency in the envelope, and diagnose the bearing fault type through the fault characteristic frequency.

Step 2.1, extract the frequency point with the most prominent pulse feature in the envelope, and obtain the peak value of the envelope spectrum in the time domain.

Step 2.1.1, the calculation formula of the short-time Fourier transform function G _STFT (i, ω _k ) of the discrete signal x(n) (n is the sampling point) in the time interval of length Nw/Fs is:

Among them, ω _k is the discrete frequency, ω _k = 2πkΔf, k = 0,..., N _w -1, g[r] is the window function, r is the current window length, N _w is the window length of the window function, F _s is the sampling frequency, i is the corresponding time point under the current window length, _ωk is the discrete frequency, x is the vibration signal, and L is the time shift between consecutive windows.

The frequency resolution of the sampling frequency F _s is as follows:

When a bearing is defective, a series of periodic pulses will be generated as its moving parts repeat the same trajectory. Considering that the pulse usually has a large bandwidth, there should be a frequency point with the most obvious time-frequency amplitude, and this frequency point should also show periodic regularity.

Step 2.1.2, calculate the envelope spectrum value of each frequency point representing the short-time Fourier transform:

Among them, φ(ω) represents the average value of short-time Fourier transform results at frequency point ω, and ζ is the fault characteristic frequency of the signal. For different types of faults, the periodicity corresponding to bearing resonance is different. Therefore, through the repetition frequency of this high-frequency resonance, that is, the fault characteristic frequency, the type of the current bearing fault can be known.

In step 2.1.3, use the maximum value to characterize the frequency point with the most prominent pulse feature in the fault signal, which is the peak value of the time-domain envelope spectrum. In order to further represent the frequency point with the most prominent pulse characteristics in the fault signal, the maximum value of the result of formula (8) can be taken, and its expression is:

Among them, TFES(ω) is the envelope spectrum value of the frequency point, here is the peak value of the envelope spectrum in the time domain, and the peak value of the envelope spectrum in the time domain has periodicity and regularity. ω is the frequency point, N _w is the window length of the window function, r is the current window length, G _STFT (i,ω _k ) is the short-time Fourier transform function, φ(ω) represents the short-time Fourier transform result in The average value at the frequency point ω, ω _k is the discrete frequency, Fs is the sampling frequency, and i is the corresponding time point under the current window length.

According to the short-time Fourier transform results of the frequency point corresponding to the peak value of the time-frequency envelope spectrum, the pulse characteristics of the bearing fault can be indicated, that is, This formula is expressed as the frequency point corresponding to the maximum value of TFES, which is used to represent the pulse characteristics of the bearing fault.

Step 2.2, extracting the time-domain envelope spectrum peak value in the envelope to form a fast time-frequency envelope spectrum peak map.

The Fast Time-Frequency Envelope Spectrum Peak Map is used to represent the peak time-frequency envelope spectrum of a signal on a two-dimensional plane of frequency and frequency resolution. The first layer of the fast time-frequency envelope spectrum peak map algorithm decomposes the signal into a binary tree structure, the second layer decomposes the signal into a 1/3 tree structure, and the rest are obtained by analogy. The decomposition structure diagram is shown in Figure 11. The Fast Time-Frequency Envelope Spectrogram is as accurate in identifying the center frequency of periodic pulsed signals as the Time-Frequency Envelope Spectrum Peak, so when demodulating the selected resonant frequency band, the Fast Time-Frequency Envelope Spectrogram can obtain more cycles pulse information. The fast time-frequency envelope spectrogram detects and characterizes the non-stationarity of the signal, and adaptively selects the best bandpass filter band as the preprocessing of the envelope spectrum analysis. The process is to find the optimal solution of the combination of frequency and frequency resolution (determined by the window length) on the entire plane, so as to determine the frequency band position and interval of the periodic transient shock component. Different types of faults often cause the system to vibrate or respond abnormally in specific frequency bands. By analyzing the frequency band location and spacing of these shock components, signatures associated with specific failure modes can be identified and can also help determine the location of the fault in the system. In the noisy working environment of rotating machinery, this method can more accurately extract the fault characteristic frequency of the bearing and effectively diagnose the health of the machinery.

Step 2.3, calculating the short-time Fourier transform result of the frequency point corresponding to the peak value of the time-domain envelope spectrum, which is the fault characteristic frequency.

Suppose the expression of the harmonic signal _x1 is Then the result of its short-time Fourier transform is

Among them, τ is the process variable, and g(v) is the window function.

The peak value of the time-frequency envelope spectrum can resist the interference of harmonic signals, which can be deduced theoretically:

Substituting the expression of formula (6) into formula (5), the following expression can be obtained:

This formula proves that the time-frequency envelope spectrum peak method proposed in this embodiment can resist the interference of harmonic signals.

Step 2.4, the envelope analysis technique is a very effective signal analysis technique in the field of bearing early fault detection and diagnosis. The main challenge of envelope analysis is to find the most suitable frequency band for demodulation. In order to evaluate the ability of the time-frequency envelope spectrum peak in identifying periodic pulse signals, the following formula is proposed:

Among them, f ₀ represents the center frequency of the periodic pulse signal, and ER is the error rate between the center frequency of the periodic pulse signal identified by TFES and the inherent center frequency of the periodic pulse signal.

Formula (8) uses the error rate between the center frequency of the periodic pulse signal located by the peak of the time-frequency envelope spectrum and the inherent center frequency of the periodic pulse signal to verify the accuracy of periodic pulse signal identification.

In this embodiment, after the obvious fault characteristic frequency is obtained, the specific fault type is diagnosed according to the fault identification method in the prior art, which will not be repeated here.

In a specific implementation, this embodiment compares the time-frequency envelope spectrum peak analysis and spectrum kurtosis analysis:

The time domain representation of the constructed composite signal is as follows:

Wherein, the signal _x1 is a sinusoidal signal with a frequency of 300Hz, and the signal _x2 is a periodic pulse signal with a center frequency of 200Hz. Signal x is a composite of signal _x1 and signal _x2 . The sampling frequency is 1000Hz, and the sampling time is 2s. Figure 2 shows the time-domain waveform, short-time Fourier transform results and spectral kurtosis results of the composite signal when the signal-to-noise ratio is 5dB, where (a) in Figure 2 is the time-domain waveform, (b) is the short-term Time Fourier transform results and spectral kurtosis results. Figure 3 shows the time-domain waveform, short-time Fourier transform results and spectral kurtosis results of the composite signal when the signal-to-noise ratio is 0dB, where (a) in Figure 3 is the time-domain waveform, (b) is the short-term Time Fourier transform results and spectral kurtosis results. Fig. 4 is the time-domain waveform, short-time Fourier transform result and spectral kurtosis result of this composite signal when the signal-to-noise ratio is -5dB, wherein, (a) in Fig. 4 is the time-domain waveform, (b) is Short-time Fourier transform results and spectral kurtosis results. It can be seen from Figures 2, 3, and 4 that as the noise intensity increases, the value of spectral kurtosis decreases significantly. However, from the time-frequency representation of the short-time Fourier transform results, as the noise intensity increases, the periodicity of the periodic pulse signal is only slightly weakened, and it can still be identified. For a clearer effect, Figure 5 plots the time-frequency envelopes of the short-time Fourier transform results in Figures 2, 3, and 4 at a frequency f=200Hz, where (a) in Figure 5 is a composite signal When the signal-to-noise ratio is 5dB, the time-frequency envelope of the short-time Fourier transform result at the frequency f=200Hz, (b) is the short-time Fourier transform result of the composite signal at the frequency f= when the signal-to-noise ratio is 0dB The time-frequency envelope at 200Hz, (c) is the time-frequency envelope of the short-time Fourier transform result of the composite signal at the frequency f=200Hz when the signal-to-noise ratio is -5dB. It can be seen from Figure 5 that as the signal-to-noise ratio decreases, the periodic feature at the center frequency of the periodic pulse signal fluctuates slightly, but it can still be identified. The composite signal shown in formula (9) is processed by the proposed method. Figure 6 is the time-domain waveform, short-time Fourier transform result and time-frequency envelope spectrum peak result of the composite signal when the signal-to-noise ratio is 5dB, wherein (a) in Figure 6 is the time-domain waveform (b) is Short-time Fourier transform results and time-frequency envelope peak results. Figure 7 is the time-domain waveform, short-time Fourier transform result and time-frequency envelope spectrum peak result of the composite signal when the signal-to-noise ratio is 0dB, wherein (a) in Figure 7 is the time-domain waveform (b) is Short-time Fourier transform results and time-frequency envelope peak results. Figure 8 is the time-domain waveform, short-time Fourier transform result and time-frequency envelope spectrum peak result of the composite signal when the signal-to-noise ratio is -5dB, wherein (a) in Figure 8 is the time-domain waveform (b) is the result of the short-time Fourier transform and the peak value of the time-frequency envelope spectrum. The results in Figures 6, 7, and 8 show that for the selected three different SNRs, the peak of the time-frequency envelope spectrum can identify the center frequency of the periodic pulse signal very accurately, and with the increase of the noise intensity , the magnitude of the peak of the time-frequency envelope spectrum does not change. In order to further evaluate the effectiveness of the time-frequency envelope spectrum peaking technique, it verifies the accuracy of locating the center frequency of the periodic transient component in the multi-component signal under a larger SNR range (from -10dB to 20dB). The results are shown in Fig. 9, in the above cases, the error rate of the peak value of the time-frequency envelope spectrum is within 1%. Therefore, it can be considered that the fast time-frequency envelope spectrum peak map has good anti-noise ability. It can accurately locate the center frequency of periodic pulse signals in the presence of noise and other disturbances.

In a specific implementation, this embodiment compares the fast time-frequency envelope spectrum peak diagram and the fast spectrum kurtosis diagram:

The time domain representation of the constructed composite signal is as follows:

This embodiment uses several simulated signals to simulate information that may exist in the acquisition. Signal A is a sinusoidal function with an amplitude of 0.3 and a frequency of 1000Hz. Signal B is the sum of a single pulse with a center frequency of 2000Hz and a single pulse with a center frequency of 4000Hz. Signal C is a periodic pulse with a center frequency of 3000Hz and a bandwidth of 150Hz. , the signal D is random noise with a signal-to-noise ratio of -7dB, and the signal S(t) is a mixture from the above signals. The waveforms of sinusoidal signals, sporadic pulses, periodic pulse signals, Gaussian white noise, mixed signals and the short-time Fourier transform results of mixed signals are shown in Figure 10. The sampling frequency of the simulation signal is 10000Hz, the sampling time is 1s, and the signal-to-noise ratio of the mixed signal S(t) is -7.8dB. S(t) is analyzed by using the fast time-frequency envelope spectrum peak map method proposed in this embodiment, and the periodic pulse signal C is extracted. The first level level0 in the fast time-frequency envelope spectrum peak map algorithm is the original signal to be analyzed. The second level level1 decomposes the signal into a binary tree structure, the third level1.6 decomposes the signal into a 1/3 tree structure; the fourth level2 decomposes the signal into a binary tree structure, and the fifth level2.6 decomposes the signal into The ternary tree structure, and the rest of the layers are analogous, and its decomposition structure is shown in Figure 11. The original signal is used as the source to be classified, named level 0. The frequency band is Δf ∈ [0, fs/2], where fs is the sampling frequency. Divide level0 into two parts, low frequency and high frequency, called level1. The frequency bands of these two parts are [0, fs/4] and [fs/4, fs/2] respectively. Divide level 0 into three parts: low frequency, intermediate frequency and high frequency, called level 1.6. The frequency bands of these three parts are [0, fs/6], [fs/6, fs/3], [fs/3, fs/2], the second level level 1 is to decompose the entire frequency band into ²¹ parts, the first The three-layer level 1.6 is to decompose the entire frequency band into 2 ^1.6 parts, and the level k is to decompose the entire frequency band into 2 ^k parts. Therefore, level k divides level 0 into 2 ^k parts, and the boundary of the lowest frequency component corresponding to level k is [0, fs/2 ^k+1 ]. Fig. 12 is the result of fast time-frequency envelope spectrum peak map processing signal S(t), the time domain waveform of the filtered signal and the squared envelope spectrum of the filtered signal, wherein (a) in Fig. 12 is the fast spectral kurtosis Figure (where SKmax is the maximum spectral kurtosis value, Bw is the bandwidth, fc is the center frequency), (b) is the time domain waveform of the filtered signal, (c) is the square envelope spectrum of the filtered signal. From the fast time-frequency envelope spectrum peak diagram, it can be seen that the frequency band with the largest time-frequency envelope spectrum peak value is at the fifth level, that is, the 20th frequency band from the left, and the frequency range is [2968.75Hz, 3125Hz]. The center frequency (f _c ) of this frequency band is 3046 Hz, the bandwidth (Bw) is 156 Hz, and the maximum value (TFESmax) of the corresponding time-frequency envelope spectrum peak value is 6.8. In the time domain of the filtered signal in Figure 12, it can be seen that the periodic pulse interval t≈0.05s. The squared envelope spectrum of the filtered signal in Fig. 12 also clearly shows the fault characteristic frequency (fe=20Hz) of the periodic pulse and its higher harmonics (nf _e ).

As a comparison, this embodiment also uses a fast spectral kurtosis graph based on spectral kurtosis to analyze the simulation signal S(t) as shown in FIG. 13 . As shown in Figure 13, the fast spectral kurtosis diagram is misled by monopulse interference. The frequency band with the largest spectral kurtosis value (SKmax) is in the frequency band with the center frequency of the fifth level at 3984 Hz, and the filtered time domain signal and its envelope square spectrum Fault characteristic frequencies of periodic pulses and periodic pulses are hardly observed in . This embodiment attempts to demodulate the second possible frequency band of the fast spectral kurtosis map, namely [2968Hz, 3125Hz]. Figure 14 shows the time-domain signal and its squared envelope spectrum after filtering in this frequency band, from which the characteristics of periodic pulses can be identified. However, compared with the decomposition results of the fast time-frequency envelope spectrum peak map in Figure 12, it can be clearly seen that the periodic impact of the time-domain signal filtered by the fast time-frequency envelope spectrum peak map is more obvious, and its square envelope The fault characteristic frequency (fe) in the spectrum is also more prominent.

In this embodiment, the bearing fault diagnosis method is verified experimentally, and the specific process is as follows:

The vibration signal used in this experiment comes from the outer ring of a variable speed bearing. The vibration signal acquisition device consists of a motor, an AC drive to control the rotational speed, an accelerometer to collect vibration data, and an incremental encoder to measure the rotational speed of the shaft. According to the structural parameters of the bearing, it can be known that the fault characteristic frequency of the outer ring of the bearing is f _o = _3.57fr ( _fr is the rotation frequency of the shaft). The sampling frequency set in the experiment is 200,000Hz. Vibration data was obtained from a faulty bearing outer race with a defect whose rotational frequency increased from 13.3 Hz to 26.3 Hz in 10 seconds. Figure 15 plots the waveform of the vibration signal and its short-time Fourier transform result. An approximate distribution of vibration signal frequencies can be observed in the short-time Fourier transform results.

The signal is processed with a fast time-frequency envelope peak map method. As shown in (a) in Figure 16, the maximum value of the peak value of the time-frequency envelope spectrum appears in the first frequency band of the fifth level, the center frequency of this frequency band is 1562.5 Hz, and the bandwidth is 3125 Hz. The time-domain waveform of the signal filtered out in this frequency band is shown in (b) in Figure 16. It can be seen that the filtered component waveform is less noisy than the original vibration signal waveform. Since the experimental data are taken from the test bench under variable speed conditions, the bearing speed is constantly changing, that is, the fault characteristic frequency of the outer ring of the bearing is also changing. The accuracy of the result can be judged by observing the short-time Fourier transform result of the square envelope spectrum of the filtered signal component, as shown in (c) in FIG. 16 . The fault characteristic frequency (f _o ) of the outer ring of the bearing, twice the rotational frequency ( _2fr ) and its difference (f _o -2f _r ) can be clearly seen in the figure, and they are all consistent with the calculation results of known parameters unanimous. This means that the fast time-frequency envelope spectrum peak map can be used to diagnose the fault of the outer ring of the variable speed bearing. Subsequently, the result of processing the signal using the fast spectral kurtosis map is shown in (a) in Fig. 17 . The component with the largest spectral kurtosis value in the fast spectral kurtosis graph is located in the second frequency band of level 1. The center frequency of this band is 75000Hz and the bandwidth is 50000Hz. The time-domain waveforms of the filtered components and the short-time Fourier transform results of their squared envelope spectra are plotted in (b) and (c) in Fig. 17, respectively. In (c) in Fig. 17, only the rotational frequency can be vaguely identified, but there is no frequency related to the fault characteristic frequency of the bearing outer ring. Therefore, this proves that the fast time-frequency envelope spectrum peak map can obtain more and more effective periodic pulse information when demodulating the selected resonant frequency band.

Embodiment two:

Embodiment 2 of the present invention provides a bearing fault diagnosis system based on time-frequency envelope spectrum peak analysis, including:

The signal acquisition module is configured to acquire the time-frequency spectrum of the vibration signal to be diagnosed, preprocess the time-frequency spectrum, and obtain the preprocessed envelope;

The fast time-frequency envelope spectrum peak map module is configured to construct a fast time-frequency envelope spectrum peak map, and utilizes the fast time-frequency envelope spectrum peak map to decompose the preprocessed envelope;

The fault diagnosis module is configured to extract the fault characteristic frequency in the envelope, and diagnose the bearing fault type through the fault characteristic frequency;

Wherein, the fault diagnosis module includes an envelope analysis module, which is configured to extract the fault characteristic frequency in the fast time-frequency envelope spectrum peak diagram. The specific steps include: extracting the frequency point with the most prominent pulse feature in the fast time-frequency envelope spectrum peak diagram. , to obtain the peak value of the time-domain envelope spectrum; calculate the short-time Fourier transform result of the frequency point corresponding to the peak value of the time-domain envelope spectrum, which is the fault characteristic frequency.

Embodiment three:

Embodiment 3 of the present invention provides a medium on which a program is stored. When the program is executed by a processor, the steps in the bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis as described in Embodiment 1 of the present invention are implemented. .

Embodiment four:

Embodiment 4 of the present invention provides a device, which includes a memory, a processor, and a program stored in the memory and operable on the processor. When the processor executes the program, the device described in Embodiment 1 of the present invention Steps in a Bearing Fault Diagnosis Method Based on Time-Frequency Envelope Spectrum Peak Analysis.

The steps involved in the above embodiments 2, 3 and 4 correspond to the method embodiment 1, and for specific implementation methods, please refer to the relevant description part of the embodiment 1. The term "computer-readable storage medium" shall be construed to include a single medium or multiple media including one or more sets of instructions; and shall also be construed to include any medium capable of storing, encoding, or carrying A set of instructions to execute and cause the processor to execute any method in the present invention.

Those skilled in the art should understand that each module or each step of the present invention described above can be realized by a general-purpose computer device, optionally, they can be realized by a program code executable by the computing device, thereby, they can be stored in a memory The device is executed by a computing device, or they are made into individual integrated circuit modules, or multiple modules or steps among them are made into a single integrated circuit module for realization. The invention is not limited to any specific combination of hardware and software.

Although the specific implementation of the present invention has been described above in conjunction with the accompanying drawings, it is not a limitation to the protection scope of the present invention. Those skilled in the art should understand that on the basis of the technical solution of the present invention, those skilled in the art do not need to pay creative work Various modifications or variations that can be made are still within the protection scope of the present invention.

Claims (10)

1. a bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis, is characterized in that, comprises the following steps: Obtain the time spectrum of the vibration signal to be diagnosed, preprocess the time spectrum, and obtain the preprocessed envelope; Construct a fast time-frequency envelope spectrum peak map, and use the fast time-frequency envelope spectrum peak map to decompose the preprocessed envelope; Extract the fault characteristic frequency in the peak graph of the fast time-frequency envelope spectrum, and diagnose the bearing fault type through the fault characteristic frequency; Among them, the specific steps of extracting the fault characteristic frequency in the fast time-frequency envelope spectrum peak map include: extracting the frequency point with the most prominent pulse feature in the fast time-frequency envelope spectrum peak map to obtain the peak value of the time domain envelope spectrum; The short-time Fourier transform result of the frequency point corresponding to the peak value of the envelope spectrum is the fault characteristic frequency. 2. The bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis as claimed in claim 1, wherein the preprocessing of the time-frequency spectrum is performing mean value removal on each frequency point in the time-frequency spectrum of the signal. 3. the bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis as claimed in claim 1, is characterized in that, extracts the most prominent frequency point of pulse feature in the envelope, obtains the specific process of time-domain envelope spectrum peak value as : Calculate the envelope spectrum value of each frequency point of the short-time Fourier transform; The frequency point with the most prominent pulse characteristics in the fault signal is represented by the maximum value, which is the peak value of the time-domain envelope spectrum. 4. the bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis as claimed in claim 3, is characterized in that, time-domain envelope spectrum peak calculation formula is: Among them, TFES(ω) is the envelope spectrum value of the frequency point, ω is the frequency point, N _w is the window length of the window function, r is the current window length, G _STFT (i,ω _k ) is the short-time Fourier transform function, φ(ω) represents the average value of the short-time Fourier transform result at the frequency point ω, ω _k is the discrete frequency, F _s is the sampling frequency, and i is the corresponding time point under the current window length. 5. the bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis as claimed in claim 4, is characterized in that, short-time Fourier transform function G _STFT (i, ω _k ) calculation formula is: Among them, ω _k is the discrete frequency, g[r] is the window function, r is the current window length, N _w is the window length of the window function, F _s is the sampling frequency, i is the corresponding time point under the current window length, ω _k is the discrete frequency, x is the vibration signal, and L is the time shift between consecutive windows. 6. The bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis according to claim 1, characterized in that the first level level0 of the fast time-frequency envelope spectrum peak map is the original signal to be analyzed. The second level level1 decomposes the signal into a binary tree structure, the third level1.6 decomposes the signal into a 1/3 tree structure; the fourth level2 decomposes the signal into a binary tree structure, and the fifth level2.6 decomposes the signal into The ternary tree structure, and so on for the rest of the layers. 7. the bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis as claimed in claim 1, is characterized in that, utilize the periodic pulse signal center frequency of time-frequency envelope spectrum peak location and the inherent center frequency of cycle pulse signal The accuracy of periodic pulse signal recognition is verified by the error rate between them. 8. A bearing fault diagnosis system based on time-frequency envelope spectrum peak analysis, characterized in that it comprises: The signal acquisition module is configured to acquire the time-frequency spectrum of the vibration signal to be diagnosed, preprocess the time-frequency spectrum, and obtain the preprocessed envelope; The fast time-frequency envelope spectrum peak map module is configured to construct a fast time-frequency envelope spectrum peak map, and utilizes the fast time-frequency envelope spectrum peak map to decompose the preprocessed envelope; The fault diagnosis module is configured to extract the fault characteristic frequency in the envelope, and diagnose the bearing fault type through the fault characteristic frequency; Wherein, the fault diagnosis module includes an envelope analysis module, which is configured to extract the fault characteristic frequency in the fast time-frequency envelope spectrum peak diagram. The specific steps include: extracting the frequency point with the most prominent pulse feature in the fast time-frequency envelope spectrum peak diagram. , to obtain the peak value of the time-domain envelope spectrum; calculate the short-time Fourier transform result of the frequency point corresponding to the peak value of the time-domain envelope spectrum, which is the fault characteristic frequency. 9. A computer-readable storage medium, wherein a plurality of instructions are stored, and the instructions are suitable for being loaded by a processor of a terminal device and executing the time-frequency-based A Bearing Fault Diagnosis Method Based on Envelope Spectrum Peak Analysis. 10. A terminal device, characterized in that it includes a processor and a computer-readable storage medium, the processor is used to implement instructions; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and Executing the bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis described in any one of claims 1-7.