CN116519301A - Bearing fault diagnosis method and system based on time-frequency envelope spectrum peak analysis - Google Patents
Bearing fault diagnosis method and system based on time-frequency envelope spectrum peak analysis Download PDFInfo
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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
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
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Periodic pulse signals serve as a basic signal and play 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 a periodic pulse signal generated by mechanical equipment damage to the bearing. However, vibration signals of rotating machine failure bearings often contain multiple components, making it difficult to predict which types of signals should be included. Periodic pulses, sporadic pulses, harmonics, non-gaussian noise, etc. containing fault information may be generated during operation of the device. How to accurately distinguish various components in a complex vibration signal, extract effective sensitive signal characteristics of faults, and accurately diagnose bearing faults is a key of current bearing fault factor analysis. One key criterion for an effective bearing failure diagnostic procedure is the ability to detect information about defects at an early stage, even in the case of machine operation noise.
Narrowband demodulation of the vibration signal makes it possible to extract the part of the rotating machine carrying the fault information. However, the quality of the demodulated signal depends on the frequency band selected for demodulation. Spectral kurtosis is currently a very efficient method and is generally considered as a powerful tool for detecting pulses. Due to the great efforts of Antoni, spectral kurtosis has been recognized as a milestone characterizing non-stationary signals, particularly bearing failure signals. Some fault diagnosis methods determine whether a periodic pulse component exists in a signal by acquiring a kurtosis index of an original signal and comparing kurtosis values. However, in the presence of random noise and monopulses, the spectral kurtosis cannot accurately detect periodic 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 not controllable, the periodic pulse components carrying fault information tend to be hidden in noise, which is difficult to separate effectively. In fact, the kurtosis value decreases with increasing pulse repetition rate. Furthermore, spectral kurtosis is sensitive to noise. And because of the limitations of its theoretical basis hypothesis, the spectral kurtosis technique is not applicable to signals obtained from variable speed experiments of the machine.
Therefore, how to quickly detect periodic pulse information in a complex signal in the bearing fault diagnosis process and simultaneously inhibit the influence of random pulse, harmonic wave and other interferences becomes a problem to be solved.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention aims to provide a bearing fault diagnosis method and system based on time-frequency envelope spectrum peak analysis, which can obtain more periodic pulse information by utilizing a rapid time-frequency envelope spectrum peak diagram, detect the periodic pulse information in complex signals, inhibit the influence of random pulse, harmonic wave and other interferences, and can more accurately extract the fault characteristic frequency of a bearing in a noisy working environment of a rotary machine, thereby effectively diagnosing the health condition of the machine.
In order to achieve the above object, the present invention is realized by the following technical scheme:
the first aspect of the invention provides a bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis, which comprises the following steps:
acquiring 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 fault characteristic frequency in a rapid time-frequency envelope spectrum peak value diagram, and diagnosing the fault type of the bearing through the fault characteristic frequency;
the specific steps of extracting the fault characteristic frequency in the rapid time-frequency envelope spectrum peak value diagram comprise: 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 the fault characteristic frequency.
Further, preprocessing the time spectrum is to perform an average removing process on each frequency point in the time spectrum of the signal.
Further, the specific process for extracting the frequency point with the most prominent pulse characteristic in the envelope to obtain the time domain envelope spectrum peak value comprises the following steps:
calculating an envelope spectrum value of each frequency point of the short-time Fourier transform;
and using the maximum value to represent a frequency point with the most prominent pulse characteristic in the fault signal, namely a time domain envelope spectrum peak value.
Further, the calculation formula of the time domain envelope spectrum peak value is:
wherein TFES (ω) is an envelope spectrum value of the frequency bin, ω is the frequency bin, N w The window length is the window function, r is the current window length, G STFT (i,ω k ) As a short-time Fourier transform function, phi (omega) represents the average value of the short-time Fourier transform result at the frequency point omega k Is of discrete frequency, F s For the sampling frequency, i is the corresponding time point under the current window length.
Further, a short-time Fourier transform function G STFT (i,ω k ) The calculation formula is as follows:
wherein omega k Is of discrete frequency, g [ r ]]Is a window function, r is the current window length, N w Window length, F, as a function of window s For sampling frequency, i is the corresponding time point, omega under the current window length k For discrete frequencies, x is the vibration signal and L is the time shift between successive windows.
Further, the first layer level0 of the fast time-frequency envelope spectrum peak value graph is an original signal to be analyzed. The second layer level1 decomposes the signal into a binary tree structure, and the third layer level1.6 decomposes the signal into a 1/3 tree structure; the fourth level2 is to decompose the signal into a binary tree structure, the fifth level2.6 is to decompose the signal into a trigeminal tree structure, the rest of the layers and so on.
Further, the error rate between the center frequency of the periodic pulse signal positioned by the time-frequency envelope spectrum peak value and the inherent center frequency of the periodic pulse signal is utilized to verify the identification accuracy of the periodic pulse signal.
The second aspect of the invention provides a bearing fault diagnosis system based on time-frequency envelope spectrum peak analysis, which comprises:
the signal acquisition module is configured to acquire a time spectrum of a vibration signal to be diagnosed, and preprocess the time spectrum to obtain a preprocessed envelope;
the rapid time-frequency envelope spectrum peak value diagram module is configured to construct a rapid time-frequency envelope spectrum peak value diagram, and the rapid time-frequency envelope spectrum peak value diagram is utilized to decompose the preprocessed envelope;
the fault diagnosis module is configured to extract fault characteristic frequencies in the envelope and diagnose the bearing fault type through the fault characteristic frequencies;
the fault diagnosis module comprises an envelope analysis module, and the specific steps for extracting the fault characteristic frequency in the rapid time-frequency envelope spectrum peak value graph comprise the following steps: 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 the fault characteristic frequency.
A third aspect of the present invention provides a medium having stored thereon a program which when executed by a processor performs the steps in a method for diagnosing a bearing failure based on analysis of a time-frequency envelope spectrum peak according to the first aspect of the present invention.
A fourth aspect of the invention provides an apparatus comprising a memory, a processor and a program stored on the memory and executable on the processor, the processor implementing the steps in a method for diagnosing a bearing failure based on analysis of a time-frequency envelope spectrum peak according to the first aspect of the invention when the program is executed.
The one or more of the above technical solutions have the following beneficial effects:
the invention discloses a bearing fault diagnosis method and a system based on time-frequency envelope spectrum peak analysis, aiming at the defect that periodic pulses cannot be accurately detected by spectral kurtosis. The method can detect the periodic pulse information in the complex signals, inhibit the influence of random pulse, harmonic wave and other interference, realize the efficient extraction of the fault characteristic frequency and finally obtain more accurate mechanical bearing fault diagnosis results.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a flowchart of a bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis in a first embodiment of the invention;
FIG. 2 is a schematic diagram of a time domain waveform, a short-time Fourier transform result and a spectral kurtosis result of a composite signal with a signal-to-noise ratio of 5dB in the first embodiment of the invention;
FIG. 3 is a schematic diagram of a time domain waveform, a short-time Fourier transform result and a spectral kurtosis result of a composite signal with a signal-to-noise ratio of 0dB in the first embodiment of the invention;
FIG. 4 is a schematic diagram of a time domain waveform, a short-time Fourier transform result and a spectral kurtosis result of a composite signal with a signal-to-noise ratio of-5 dB in a first embodiment of the invention;
fig. 5 is a time-frequency envelope diagram of a short-time fourier transform result at a frequency f=200 Hz in the first embodiment of the present invention;
FIG. 6 is a schematic diagram of a time domain waveform, a short-time Fourier transform result and a time-frequency envelope spectrum peak result of a composite signal with a signal-to-noise ratio of 5dB in the first embodiment of the invention;
FIG. 7 is a schematic diagram of a time domain waveform, a short-time Fourier transform result and a time-frequency envelope spectrum peak result of a composite signal with a signal-to-noise ratio of 0dB in the first embodiment of the invention;
FIG. 8 is a schematic diagram of a time domain waveform, a short-time Fourier transform result and a time-frequency envelope spectrum peak result of a composite signal with a signal-to-noise ratio of-5 dB in a first embodiment of the invention;
FIG. 9 is a graph showing error rates of center frequencies of periodic pulse components in a time-frequency envelope spectrum peak-positioning composite signal at different signal-to-noise ratios according to an embodiment of the present invention;
FIG. 10 is a schematic diagram showing waveforms of sinusoidal signals, sporadic pulses, periodic pulse signals, gaussian white noise, mixed signals, and short-time Fourier transform results of the mixed signals according to the first embodiment of the invention;
FIG. 11 is an exploded view of a peak spectrum of a fast time-frequency envelope in accordance with an embodiment of the present invention;
FIG. 12 is a graph of the result of processing the signal S (t) with a fast time-frequency envelope spectrum peak graph, filtering out the time-domain waveform of the signal, and filtering out the square envelope spectrum of the signal according to the first embodiment of the present invention;
FIG. 13 is a graph showing the result of processing a signal S (t) with a rapid spectral kurtosis graph, a filtered signal time domain waveform, and a filtered signal squared envelope spectrum in accordance with an embodiment of the present invention;
FIG. 14 is a graph of a filtered signal time domain waveform and a filtered signal square envelope for a second possible frequency band of a rapid spectral kurtosis plot in accordance with an embodiment of the present invention;
FIG. 15 is a waveform of vibration signals of the outer ring of the bearing collected under the medium-speed working condition and a short-time Fourier transform result thereof according to the first embodiment of the invention;
FIG. 16 is a diagram showing the result of processing a vibration signal, filtering out the time domain waveform of the signal, and filtering out the short-time Fourier transform result of the square envelope spectrum of the signal according to the first embodiment of the present invention;
FIG. 17 is a diagram showing the results of processing a vibration signal, filtering out the time domain waveform of the signal, and filtering out the short-time Fourier transform of the square envelope spectrum of the signal according to the first embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the 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 is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof;
embodiment one:
the first embodiment of the invention provides a bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis, as shown in fig. 1, comprising the following steps:
step 1, obtaining a time spectrum of a vibration signal to be diagnosed, and preprocessing the time spectrum to obtain a preprocessed envelope.
Step 1.1, preprocessing a time spectrum, namely performing mean value removal processing on each frequency point in the time spectrum of the signal, and then taking the envelope after processing.
In step 1.2, vibration signals collected from bearings of the rotating machine often include various information such as fault information, equipment operation sounds, environmental noise, and the like. The vibration characteristics of a rolling bearing with local defects can be expressed in an amplitude modulation process. The vibration signal x (t) is thus modeled:
wherein A is k Is the amplitude of the kth fault pulse, 2K is the number of pulses, v (T) is the unit step function, and the period of time corresponding to the fault signature frequency is T 0 Eta is the structural damping characteristic coefficient omega 0 Corresponding to the resonance frequency of the bearing excitation, t i As an ith implementation zero-mean uniformly distributed random variable, its standard deviation is in the range of 0.02T0. N (t) is the sum of random noise, harmonics and other interference from the surrounding environment. The random variable plays a role in the fault diagnosis of the rotary mechanical bearing by modeling and analyzing the random characteristics of related parameters such as vibration signals, sensor data and the like,thereby helping to judge whether the bearing has faults or not and providing the diagnosis basis of fault types.
And 2, extracting fault characteristic frequency in the envelope, and diagnosing the fault type of the bearing through the fault characteristic frequency.
And 2.1, extracting a frequency point with the most prominent pulse characteristic in the envelope to obtain a time domain envelope spectrum peak value.
Step 2.1.1 short time Fourier transform function G of discrete signal x (n) (n is the sampling point) in time interval of length Nw/Fs STFT (i,ω k ) The calculation formula is as follows:
wherein omega k Is of discrete frequency omega k ＝2πkΔf,k＝0,...,N w -1，g[r]Is a window function, r is the current window length, N w Window length, F, as a function of window s For sampling frequency, i is the corresponding time point, omega under the current window length k For discrete frequencies, x is the vibration signal and L is the time shift between successive windows.
Sampling frequency F s The frequency resolution of (a) is the following:
when a bearing is defective, a series of periodic pulses will be generated as its moving parts repeat the same trajectory. Considering that pulses typically have a large bandwidth, there should be a frequency bin where the time-frequency amplitude is most pronounced, which frequency bin should also exhibit periodic regularity.
Step 2.1.2, calculating an envelope spectrum value of each frequency point representing the short-time fourier transform:
where φ (ω) represents the average value of the short-time Fourier transform result at the frequency point ω, ζ is the failure characteristic frequency of the signal. The periodicity corresponding to the bearing resonance is different for different types of faults. Thus, the type of current bearing failure can be known from the repetition frequency of such high frequency resonance, i.e. the failure characteristic frequency.
And 2.1.3, representing the frequency point with the most prominent pulse characteristic in the fault signal by using the maximum value, namely the time domain envelope spectrum peak value. To further represent the frequency point in the fault signal where the pulse feature is most prominent, the maximum value of the result of equation (8) may be expressed as:
wherein TFES (ω) is an envelope spectrum value of the frequency point, here a time-domain envelope spectrum peak, which has periodicity and regularity. Omega is the frequency point, N w The window length is the window function, r is the current window length, G STFT (i,ω k ) As a short-time Fourier transform function, phi (omega) represents the average value of the short-time Fourier transform result at the frequency point omega k For discrete frequencies, fs is the sampling frequency, and i is the corresponding time point for the current window length.
The pulse characteristics of bearing faults can be indicated according to the short-time Fourier transform result of the frequency point corresponding to the time-frequency envelope spectrum peak value, namelyThe formula is expressed as the corresponding frequency point where the TFES value is maximum is used to characterize the pulse of the bearing failure.
And 2.2, extracting a time domain envelope spectrum peak value in the envelope to form a rapid time-frequency envelope spectrum peak value diagram.
The fast time-frequency envelope spectrum peak diagram is used for representing the time-frequency envelope spectrum peak value of the signal on a frequency and frequency resolution two-dimensional plane. The first layer of the rapid time-frequency envelope spectrum peak value graph algorithm decomposes the signal into a binary tree structure, the second layer decomposes the signal into a 1/3 tree structure, and the rest is obtained by analogy, and the decomposition structure diagram is shown in fig. 11. The rapid time-frequency envelope spectrum peak value graph is as accurate as the time-frequency envelope spectrum peak value in the aspect of identifying the center frequency of the periodic pulse signal, so that the rapid time-frequency envelope spectrum graph can obtain more periodic pulse information when demodulating the selected resonant frequency band. The rapid time-frequency envelope spectrogram adaptively selects the optimal band-pass filtered band as a pretreatment for envelope spectrum analysis by detecting and characterizing the non-stationarity of the signal. The process is to find the optimal solution of the combination of frequency and frequency resolution (determined by window length) across the whole plane to determine the band location and spacing of the periodic transient impulse components. Different types of faults often cause the system to experience abnormal vibrations or responses in particular frequency bands. By analyzing the band location and spacing of these impact components, characteristic signals associated with particular failure modes can be identified, and can also help determine the location of the failure in the system. In a noisy working environment of the rotary machine, the method can more accurately extract the fault characteristic frequency of the bearing, and effectively diagnose the health condition of the machine.
And 2.3, calculating a short-time Fourier transform result of a frequency point corresponding to the time domain envelope spectrum peak value, namely the fault characteristic frequency.
Assuming harmonic signal x 1 Is expressed asThe result of the short-time Fourier transform is
Where τ is the process variable and g (v) is the window function.
The time-frequency envelope spectrum peak value can resist the interference of harmonic signals, which can be deduced from theory:
substituting the expression of formula (6) into formula (5) can result in the following expression:
the formula proves that the time-frequency envelope spectrum peak value method provided by the embodiment can resist the interference of harmonic signals.
In step 2.4, the envelope analysis technique is a very effective signal analysis technique in the field of early failure detection and diagnosis of bearings. The main challenge of envelope analysis is to find the frequency band most suitable for demodulation. In order to evaluate the ability of the time-frequency envelope spectrum peak to identify periodic pulse signals, the following formula is proposed:
wherein 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.
And (8) verifying the identification accuracy of the periodic pulse signal by using the error rate between the center frequency of the periodic pulse signal positioned by the peak value of the time-frequency envelope spectrum and the inherent center frequency of the periodic pulse signal.
In this embodiment, after the obvious fault characteristic frequency is obtained, a specific fault type is diagnosed according to a fault identification manner in the prior art, which is not described herein.
In a specific implementation manner, the embodiment compares a time-frequency envelope spectrum peak analysis with a spectrum kurtosis analysis:
the constructed composite signal time domain is represented as follows:
wherein the signal x 1 Is a sinusoidal signal with a frequency of 300Hz, signal x 2 Is a periodic pulse signal with a center frequency of 200 Hz. Signal x is signal x 1 Sum signal x 2 Is a complex of (a) and (b). Wherein the sampling frequency is 1000Hz and the sampling time is 2s. FIG. 2 shows a time domain waveform of the composite signal at a signal-to-noise ratio of 5dBLeaf transform results and spectral kurtosis results, where (a) in fig. 2 is a time domain waveform and (b) is a short-time fourier transform result and spectral kurtosis result. Fig. 3 shows a time domain waveform, a short time fourier transform result and a spectral kurtosis result of the composite signal when the signal-to-noise ratio is 0dB, wherein (a) in fig. 3 is the time domain waveform, and (b) is the short time fourier transform result and the spectral kurtosis result. Fig. 4 shows a time domain waveform, a short time fourier transform result and a spectral kurtosis result of the composite signal at a signal-to-noise ratio of-5 dB, wherein (a) in fig. 4 is the time domain waveform and (b) is the short time fourier transform result and the spectral kurtosis result. As can be seen from fig. 2, 3, 4, the value of the spectral kurtosis decreases significantly with increasing noise intensity. However, from the time-frequency representation of the short-time fourier transform result, the periodicity of the periodic pulse signal is only slightly reduced with increasing noise intensity and can still be identified. For clearer effects, fig. 5 plots the time-frequency envelope of the short-time fourier transform result at the frequency f=200 Hz in fig. 2, 3, and 4, respectively, where (a) in fig. 5 is the time-frequency envelope of the short-time fourier transform result at the frequency f=200 Hz when the signal-to-noise ratio is 5dB, (b) is the time-frequency envelope of the short-time fourier transform result at the frequency f=200 Hz when the signal-to-noise ratio is 0dB, and (c) is the time-frequency envelope of the short-time fourier transform result at the frequency f=200 Hz when the signal-to-noise ratio is-5 dB. It can be seen from fig. 5 that the periodic characteristics at the center frequency of the periodic pulse signal slightly fluctuate with decreasing signal-to-noise ratio, but can still be identified. The composite signal shown in equation (9) is processed using the proposed method. Fig. 6 shows a time domain waveform, a short time fourier transform result and a time-frequency envelope spectrum peak result of the composite signal at a signal-to-noise ratio of 5dB, wherein (a) in fig. 6 shows a time domain waveform (b) as a short time fourier transform result and a time-frequency envelope spectrum peak result. Fig. 7 shows a time domain waveform, a short time fourier transform result and a time-frequency envelope spectrum peak result of the composite signal at a signal-to-noise ratio of 0dB, wherein (a) in fig. 7 shows a time domain waveform (b) as a short time fourier transform result and a time-frequency envelope spectrum peak result. FIG. 8 shows the time domain waveform, the short time Fourier transform result and the time-frequency envelope spectrum peak result of the composite signal at a signal-to-noise ratio of-5 dB, wherein (a) in FIG. 8 is the time domain waveform (b)The result is a short-time Fourier transform result and a time-frequency envelope spectrum peak value result. The results of fig. 6, 7, 8 show that the time-frequency envelope spectrum peak can very accurately identify the center frequency of the periodic pulse signal for all three different signal-to-noise ratios selected, and that the magnitude of the time-frequency envelope spectrum peak does not change as the noise intensity increases. 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 over a larger signal-to-noise ratio range (from-10 dB to 20 dB). As a result, as shown in fig. 9, in the above case, the error rate of the time-frequency envelope spectrum peak was 1% or less. Therefore, the fast time-frequency envelope spectrum peak diagram can be considered to have good noise immunity. It can accurately locate the center frequency of a periodic pulse signal in the presence of noise and other disturbances.
In a specific implementation manner, the fast time-frequency envelope spectrum peak value diagram and the fast spectrum kurtosis diagram are compared in the embodiment:
the constructed composite signal time domain is represented as follows:
the present embodiment uses several simulation signals to simulate information that may be present in the acquisition. Signal a is a sine function with an amplitude of 0.3 and a frequency of 1000Hz, signal B is the sum of a single pulse with a centre frequency of 2000Hz and a single pulse with a centre frequency of 4000Hz, signal C is a periodic pulse with a centre frequency of 3000Hz and a bandwidth of 150Hz, signal D is random noise and a signal-to-noise ratio of-7 dB, and signal S (t) is a mixture from the above signals. The waveforms of the sinusoidal signal, the sporadic pulse, the periodic pulse signal, the gaussian white noise, the mixed signal, and the short-time fourier transform result of the mixed signal are shown in fig. 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-7.8 dB. S (t) is analyzed by using the rapid time-frequency envelope spectrum peak value graph method provided by the embodiment, and a periodic pulse signal C is extracted. First in fast time-frequency envelope spectrum peak graph algorithmLayer level0 is the original signal to be analyzed. The second layer level1 decomposes the signal into a binary tree structure, and the third layer level1.6 decomposes the signal into a 1/3 tree structure; the fourth layer level2 is to decompose the signal into a binary tree structure, the fifth layer level2.6 is to decompose the signal into a three-tree structure, the rest layers are the same, and the decomposition structure diagram is shown in fig. 11. The original signal is named level0 as the source to be classified. The frequency band is Deltaf E [0, fs/2]Where fs is the sampling frequency. Level0 is divided into two parts, low and high frequency, called level1. The frequency bands of the two parts are respectively [0, fs/4 ]]And [ fs/4, fs/2]. Level0 is divided into three parts, low frequency, intermediate frequency and high frequency, called level 1.6. The frequency bands of the three parts are [0, fs/6 ]]、[fs/6,fs/3]、[fs/3,fs/2]The second layer level1 decomposes the entire band into 2 1 The third layer level1.6 divides the whole band into 2 1.6 Level k divides the entire band into 2 k Partial regularity. Thus, level k divides level0 into 2 k The boundary of the lowest frequency component corresponding to level k is [0, fs/2 ] k+1 ]. Fig. 12 shows the result of processing the signal S (t) with the fast time-frequency envelope spectrum peak value graph, the time-domain waveform of the filtered signal, and the square envelope spectrum of the filtered signal, where (a) in fig. 12 is the fast spectral kurtosis graph (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, and (c) is the square envelope spectrum of the filtered signal. As can be seen from the fast time-frequency envelope spectrum peak diagram, the frequency band with the maximum time-frequency envelope spectrum peak is positioned at the 5 th level, namely the 20 th frequency band from the left, and the frequency range is [2968.75Hz,3125Hz]. The center frequency (f c ) The bandwidth (Bw) is 156Hz at 3046Hz, and the corresponding maximum value of the time-frequency envelope spectrum peak (tfesm) is 6.8. The periodic pulse interval t≡0.05s can be seen in the time domain of the filtered signal of fig. 12. The square envelope spectrum of the filtered signal in fig. 12 also clearly shows the failure characteristic frequency of the periodic pulses (fe=20 Hz) and its higher harmonics (nf e )。
In contrast, the present embodiment also uses a rapid spectral kurtosis graph based on spectral kurtosis to analyze the simulation signal S (t) as in fig. 13. As shown in FIG. 13, the rapid spectral kurtosis diagram is misled by single pulse interference, the frequency band with the maximum spectral kurtosis value (SKmax) is in the frequency band with the 5 th-level center frequency of 3984Hz, and the filtered time domain signal and the envelope square spectrum thereof have almost no observed periodic pulse and fault characteristic frequency of the periodic pulse. This embodiment attempts to demodulate the second possible band of the fast spectral kurtosis map, i.e. [2968Hz,3125Hz ]. Fig. 14 shows the frequency band filtered time domain signal and its square envelope spectrum, from which the characteristics of the periodic pulses can be identified. However, compared with the decomposition result of the fast time-frequency envelope spectrum peak diagram in fig. 12, it can be clearly seen that the periodic impact of the time domain signal after the fast time-frequency envelope spectrum peak diagram is more obvious, and the fault characteristic frequency (fe) in the square envelope spectrum is also more obvious.
The embodiment performs experimental verification on the bearing fault diagnosis method, and the specific process is as follows:
the vibration signal used in this experiment was from the outer race of a variable speed bearing. The vibration signal acquisition device comprises a motor, an alternating current driver for controlling the rotating speed, an accelerometer for collecting vibration data and an incremental encoder for measuring the rotating speed of the shaft. According to the structural parameters of the bearing, the failure characteristic frequency of the outer ring of the bearing is known to be f o ＝3.57f r (f r Is the rotational frequency of the shaft). The sampling frequency set in the experiment was 200,000Hz. Vibration data from a defective, faulty bearing cup increased in rotational frequency from 13.3Hz to 26.3Hz in 10 seconds. Fig. 15 plots the waveform of the vibration signal and the short-time fourier transform result thereof. The approximate distribution of vibration signal frequencies may be observed in the short-time fourier transform results.
The signal is processed by a fast time-frequency envelope spectrum peak diagram method. As shown in (a) of fig. 16, the maximum value of the time-frequency envelope spectrum peak occurs in the first frequency band of the 5 th stage, which has a center frequency of 1562.5Hz and a bandwidth of 3125Hz. The time domain waveform of the signal filtered out of this band is shown in fig. 16 (b). 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 a test bed under the condition of variable speed, the bearing rotating speed is continuously changed, namely the failure characteristic frequency of the outer ring of the bearing is also changed. By observing the level of filtered signal componentsThe accuracy of the result is judged by the short-time fourier transform result of Fang Baolao spectrum, as shown in fig. 16 (c). Failure characteristic frequency (f) of bearing outer race o ) 2 times the rotation frequency (2 f r ) And the difference (f) o -2f r ) As can be clearly seen in the figure, and are consistent with the results of the calculation of known parameters. This means that the fast time-frequency envelope spectrum peak map can be used to diagnose a failure of the outer race of the variable speed bearing. Subsequently, the signal result is processed using the rapid spectral kurtosis graph as shown in fig. 17 (a). The component with the largest spectral kurtosis value in the fast spectral kurtosis diagram is located in the 2 nd frequency band of the level1. The center frequency of the frequency band is 75000Hz, and the bandwidth is 50000Hz. The time domain waveform of the filtered component and the short time fourier transform result of its square envelope spectrum are plotted in fig. 17 (b) and (c), respectively. In fig. 17 (c), only the rotation frequency can be recognized with ambiguity, but there is no frequency related to the failure characteristic frequency of the bearing outer race. This therefore demonstrates that a fast time-frequency envelope spectrum peak plot can obtain more and more efficient periodic pulse information when demodulating the selected resonant frequency band.
Embodiment two:
the second embodiment of the invention provides a bearing fault diagnosis system based on time-frequency envelope spectrum peak analysis, which comprises:
the signal acquisition module is configured to acquire a time spectrum of a vibration signal to be diagnosed, and preprocess the time spectrum to obtain a preprocessed envelope;
the rapid time-frequency envelope spectrum peak value diagram module is configured to construct a rapid time-frequency envelope spectrum peak value diagram, and the rapid time-frequency envelope spectrum peak value diagram is utilized to decompose the preprocessed envelope;
the fault diagnosis module is configured to extract fault characteristic frequencies in the envelope and diagnose the bearing fault type through the fault characteristic frequencies;
the fault diagnosis module comprises an envelope analysis module, and the specific steps for extracting the fault characteristic frequency in the rapid time-frequency envelope spectrum peak value graph comprise the following steps: 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 the fault characteristic frequency.
Embodiment III:
an embodiment III of the present invention provides a medium having a program stored thereon, which when executed by a processor, implements the steps in the bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis according to the embodiment I of the present invention.
Embodiment four:
the fourth embodiment of the invention provides a device, which comprises a memory, a processor and a program stored in the memory and capable of running on the processor, wherein the processor realizes the steps in the bearing fault diagnosis method based on the time-frequency envelope spectrum peak analysis according to the first embodiment of the invention when executing the program.
The steps involved in the second, third and fourth embodiments correspond to the first embodiment of the method, and the detailed description of the second embodiment refers to the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media including one or more sets of instructions; it should also be understood to include any medium capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any one of the methods of the present invention.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented by general-purpose computer means, alternatively they may be implemented by program code executable by computing means, whereby they may be stored in storage means for execution by computing means, or they may be made into individual integrated circuit modules separately, or a plurality of modules or steps in them may be made into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (10)
1. The bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis is characterized by comprising the following steps of:
acquiring 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 fault characteristic frequency in a rapid time-frequency envelope spectrum peak value diagram, and diagnosing the fault type of the bearing through the fault characteristic frequency;
the specific steps of extracting the fault characteristic frequency in the rapid time-frequency envelope spectrum peak value diagram comprise: 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 the fault characteristic frequency.
2. The method for diagnosing bearing faults based on time-frequency envelope spectrum peak analysis as claimed in claim 1, wherein the preprocessing of the time spectrum is to perform a de-averaging process on each frequency point in the time spectrum of the signal.
3. The bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis according to claim 1, wherein the specific process of extracting the frequency point with the most prominent pulse characteristic in the envelope to obtain the time-domain envelope spectrum peak is as follows:
calculating an envelope spectrum value of each frequency point of the short-time Fourier transform;
and using the maximum value to represent a frequency point with the most prominent pulse characteristic in the fault signal, namely a time domain envelope spectrum peak value.
4. The bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis as set forth in claim 3, wherein the time-domain envelope spectrum peak calculation formula is:
wherein TFES (ω) is an envelope spectrum value of the frequency bin, ω is the frequency bin, N w The window length is the window function, r is the current window length, G STFT (i,ω k ) As a short-time Fourier transform function, phi (omega) represents the average value of the short-time Fourier transform result at the frequency point omega k Is of discrete frequency, F s For the sampling frequency, i is the corresponding time point under the current window length.
5. The method for diagnosing bearing faults based on time-frequency envelope spectrum peak analysis as claimed in claim 4, wherein the short time Fourier transform function G STFT (i,ω k ) The calculation formula is as follows:
wherein omega k Is of discrete frequency, g [ r ]]Is a window function, r is the current window length, N w Window length, F, as a function of window s For sampling frequency, i is the corresponding time point, omega under the current window length k For discrete frequencies, x is the vibration signal and L is the time shift between successive windows.
6. The method for diagnosing bearing faults based on time-frequency envelope spectrum peak analysis as claimed in claim 1, wherein the first layer level0 of the fast time-frequency envelope spectrum peak graph is an original signal to be analyzed. The second layer level1 decomposes the signal into a binary tree structure, and the third layer level1.6 decomposes the signal into a 1/3 tree structure; the fourth level2 is to decompose the signal into a binary tree structure, the fifth level2.6 is to decompose the signal into a trigeminal tree structure, the rest of the layers and so on.
7. The bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis according to claim 1, wherein the error rate between the center frequency of the periodic pulse signal located by the time-frequency envelope spectrum peak and the inherent center frequency of the periodic pulse signal is used for verifying the accuracy of the periodic pulse signal identification.
8. A bearing fault diagnosis system based on time-frequency envelope spectrum peak analysis, comprising:
the signal acquisition module is configured to acquire a time spectrum of a vibration signal to be diagnosed, and preprocess the time spectrum to obtain a preprocessed envelope;
the rapid time-frequency envelope spectrum peak value diagram module is configured to construct a rapid time-frequency envelope spectrum peak value diagram, and the rapid time-frequency envelope spectrum peak value diagram is utilized to decompose the preprocessed envelope;
the fault diagnosis module is configured to extract fault characteristic frequencies in the envelope and diagnose the bearing fault type through the fault characteristic frequencies;
the fault diagnosis module comprises an envelope analysis module, and the specific steps for extracting the fault characteristic frequency in the rapid time-frequency envelope spectrum peak value graph comprise the following steps: 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 the fault characteristic frequency.
9. A computer readable storage medium, characterized in that a plurality of instructions are stored, which instructions are adapted to be loaded by a processor of a terminal device and to perform the bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis according to any one of claims 1-7.
10. A terminal device comprising a processor and a computer readable storage medium, the processor configured to implement instructions; a computer readable storage medium for storing a plurality of instructions adapted to be loaded by a processor and to perform the bearing fault diagnosis method based on time-frequency envelope spectrum peak analysis of any one of claims 1-7.
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| CN117150349A (en) * | 2023-10-31 | 2023-12-01 | 济南嘉宏科技有限责任公司 | Intelligent equipment foundation fault autonomous positioning and quantitative evaluation method and system |
| CN117407692A (en) * | 2023-09-12 | 2024-01-16 | 石家庄铁道大学 | Gear periodic fault impact feature extraction method based on frequency peak filtering |
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| CN117407692A (en) * | 2023-09-12 | 2024-01-16 | 石家庄铁道大学 | Gear periodic fault impact feature extraction method based on frequency peak filtering |
| CN117150349A (en) * | 2023-10-31 | 2023-12-01 | 济南嘉宏科技有限责任公司 | Intelligent equipment foundation fault autonomous positioning and quantitative evaluation method and system |
| CN117150349B (en) * | 2023-10-31 | 2024-02-02 | 济南嘉宏科技有限责任公司 | Intelligent equipment foundation fault autonomous positioning and quantitative evaluation method and system |
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