CN116150585A - Rotary machine fault diagnosis method based on product envelope spectrum - Google Patents
Rotary machine fault diagnosis method based on product envelope spectrum Download PDFInfo
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Abstract
The invention discloses a rotary machine fault diagnosis method based on a product envelope spectrum, and relates to the technical field of mechanical equipment state monitoring and fault diagnosis. The invention designs a band-pass filter group to carry out band-pass filtering on the collected vibration acceleration signals to obtain a group of band-pass filtering signals; selecting a plurality of proper exponentials, constructing a product envelope spectrum by a generalized envelope spectrum, and then calculating the product envelope spectrum of each band-pass filtering signal; selecting theoretical fault characteristic frequency and calculating a frequency domain signal-to-noise ratio index of a product envelope spectrum of each band-pass filtering signal to obtain fault characteristic information quantization evaluation results of different spectrum bands; analyzing the product envelope spectrum of the band-pass filtering signal corresponding to the maximum frequency domain signal-to-noise ratio index, and diagnosing the fault of the rotary machine; the product envelope spectrum obtained by the invention has more remarkable fault characteristic frequency, and can effectively improve the accuracy of mechanical fault diagnosis under different interference noise pollution.
Description
Technical Field
The invention relates to the technical field of mechanical equipment state monitoring and fault diagnosis, in particular to a rotary machine fault diagnosis method based on product envelope spectrum.
Background
The rolling bearing, the gear and other rotating parts are one of essential basic parts in modern mechanical equipment, and are also one of sources of common faults of the mechanical equipment. Rolling bearings and gears have been two important research objects in the field of mechanical failure diagnosis. Local imperfections in the rolling bearings and gears often cause repetitive transient pulses in the vibration signal and result in different failure characteristic frequencies. Thus, bearing and gear failure detection and diagnosis is often accomplished by analyzing the frequency of failure signatures in the frequency spectrum of the vibration signal, which has proven to be an effective method or means. However, bearing failure signals exhibit non-stationarity, often in the form of cyclostationarity, and are characterized by frequency and amplitude modulation.
These characteristics result in a direct fourier transform of the measurement signal that generally fails to reveal the characteristic frequencies associated with machine faults. Instead, the sensor measurement signals need to be demodulated before they are fourier transformed. In the field of fault diagnosis of rotating machines, the techniques commonly used for achieving this are Envelope Spectroscopy (ES), square Envelope Spectroscopy (SES) and Logarithmic Envelope Spectroscopy (LES).
ES is the fourier transform of the signal envelope, SES is the fourier transform of the signal squared envelope, LES is the fourier transform of the signal logarithmic envelope. In these three methods, the envelope signal is typically obtained by hilbert transformation of the signal under analysis. When the interference noise is relatively weak, performing ES, SES and LES directly on the measurement signal is generally able to detect the fault signature frequency and its harmonics. However, when the measurement signal is contaminated by complex interference noise, the direct ES, SES and LES cannot effectively reveal the fault-related characteristic frequencies, and then a certain characteristic enhancement or noise reduction technique is required to preprocess the measurement signal. Common methods for achieving these objectives are bandpass filtering, blind deconvolution, signal decomposition, and the like. Among these methods, envelope analysis based on bandpass filtering and ES (or SES, LES) is a widely used rotary machine fault diagnosis method, and has been extensively explored and developed in the past two decades. However, in existing envelope analysis methods, a single "envelope spectrum" technique is used for spectral analysis to diagnose rotating machinery faults, such as an envelope spectrum, a square envelope spectrum, or a logarithmic envelope spectrum. Because of the complex operating environment of mechanical devices, sensor measurement signals often contain strong background noise and strong random impulse noise, resulting in a single "envelope spectrum" technique that is ineffective in diagnosing rotary machine faults under different operating conditions.
Disclosure of Invention
The invention aims to provide a rotary machine fault diagnosis method based on a product envelope spectrum, wherein the fault characteristic frequency in the product envelope spectrum after band-pass filtering of a measured signal is more remarkable, and the accuracy of machine fault diagnosis under different interference noise pollution can be effectively improved.
In order to achieve the technical purpose and the technical effect, the invention is realized by the following technical scheme:
the rotary machine fault diagnosis method based on the product envelope spectrum comprises the following steps:
s1: designing a band-pass filter group to carry out band-pass filtering on the collected vibration acceleration signals to obtain a group of band-pass filtering signals;
s2: selecting a plurality of proper exponentials, constructing a product envelope spectrum by a generalized envelope spectrum, and then calculating the product envelope spectrum of each band-pass filtering signal;
s3: selecting theoretical fault characteristic frequency and calculating a frequency domain signal-to-noise ratio index of a product envelope spectrum of each band-pass filtering signal to obtain fault characteristic information quantization evaluation results of different spectrum bands;
s4: and analyzing the product envelope spectrum of the band-pass filtered signal corresponding to the maximum frequency domain signal-to-noise ratio index, and diagnosing the rotating machinery fault.
Further, the S1 specifically includes:
s101: performing mean value removal pretreatment on vibration acceleration signals acquired by a sensor arranged on the rotary mechanical equipment;
s102: and designing a band-pass filter group to carry out band-pass filtering on the vibration acceleration signals after the mean value is removed, so as to obtain a group of filtering signals with different center frequencies and bandwidths.
Further, the filter bank of S102 is a 1/3-binary tree filter bank, a sliding window filter bank, a wavelet packet transform filter bank, an empirical wavelet transform filter bank or other filter banks.
Further, the step S2 specifically includes:
s201: based on the hilbert transform, an analytic signal of each band-pass filtered signal is constructed as follows:
where a (t) is an analysis signal of the band-pass filtered signal, x bp,k (t) is the kth bandpass filtered signal of the measurement signal x (t), H { · } is the Hilbert transform, j is the imaginary unit, t is time, τ is the integral variable;
s202: selecting proper power exponent p, based on simplified Box-Cox transformation, constructing generalized envelope signal v from analytic signal of band-pass filtering signal p (t); generalized envelope signal v p The calculation formula of (t) is as follows:
s203: based on the fast Fourier transform, a corresponding generalized envelope spectrum is obtained by calculation of a generalized envelope signal, and the calculation formula is as follows:
wherein S is p (f) A generalized envelope spectrum with a power exponent of p, wherein F { · } is a fast Fourier transform, and F is a frequency;
s204: selecting M suitable exponentiations, i.e. p 1 ,p 2 ,...,p M Derived from M generalized envelope spectrum constructions of each band-pass filtered signalThe corresponding product envelope spectrum is calculated as follows:
wherein S is PES (f) For the product envelope spectrum,
and->
Respectively the exponentiation of power of p m Generalized envelope and generalized envelope spectrum, p, of a time-bandpass filtered signal m Is the mth power exponent.
Further, the step S3 specifically includes:
s301: estimating a theoretical value of the fault characteristic frequency according to the size parameter and the rotating speed of the rotating mechanical parts;
s302: calculating the frequency domain signal-to-noise ratio index of the product envelope spectrum of each band-pass filtering signal, and estimating the richness of fault characteristic information of different spectrum bands; the calculation formula of the frequency domain signal to noise ratio is as follows:
wherein I is FDSNR For the frequency domain signal-to-noise ratio, H is the number of harmonics of the fault signature frequency of interest; omega shape h Representing a frequency f of failure m Is centered on the h harmonic of (a) and comprises the frequency hf m And a plurality of frequency components on both sides thereof; s is S PES (α) is the magnitude of the product envelope spectrum at frequency α; f (f) l For the first spectral frequency; s is S PES (f l ) Envelope the spectrum at frequency f for the product l Amplitude at; l is the number of spectral frequencies of interest.
Further, the maximum value f of the spectral band range of the product envelope spectrum involved in the calculation L Should be at least greater than the fault signature frequency f m 3 times of (3).
Further, the step S4 specifically includes:
s401: comparing the frequency domain signal-to-noise ratio indexes of the filtering signals of different spectrum bands, and identifying the product envelope spectrum of the band-pass filtering signals corresponding to the maximum value of the frequency domain signal-to-noise ratio indexes, namely the optimal product envelope spectrum;
s402: and analyzing frequency components in the optimal product envelope spectrum, and diagnosing faults of the rotary machine according to the fault characteristic frequency.
The invention has the beneficial effects that:
the invention relates to a rotary machinery fault diagnosis method based on product envelope spectrum, which comprises the steps of designing a band-pass filter bank to carry out band-pass filtering on collected vibration acceleration signals to obtain a group of band-pass filtering signals; selecting a plurality of proper exponentials, constructing a product envelope spectrum by a generalized envelope spectrum, and then calculating the product envelope spectrum of each band-pass filtering signal; generalized envelope spectra with different exponentiations exhibit different performance characteristics for different interference noise; meanwhile, the product envelope spectrum combines the advantages of different generalized envelope spectrums, so that not only can the periodic modulation component in the signal be enhanced, but also the non-periodic interference noise component in the signal can be eliminated, and the identification of the fault characteristic frequency and the harmonic wave thereof is facilitated;
the invention is based on the rotary machinery fault diagnosis method of the product envelope spectrum, select the theoretical fault characteristic frequency and calculate the frequency domain signal-to-noise ratio index of the product envelope spectrum of each band-pass filtering signal, get the fault characteristic information quantization assessment result of different spectral bands; analyzing the product envelope spectrum of the band-pass filtering signal corresponding to the maximum frequency domain signal-to-noise ratio index, and diagnosing the fault of the rotary machine; the fault characteristic frequency in the product envelope spectrum of the measurement signal after band-pass filtering is more remarkable, and the accuracy of mechanical fault diagnosis under different interference noise pollution can be effectively improved.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.
Drawings
FIG. 1 is a flow chart of a method for diagnosing a rotary machine fault based on a product envelope spectrum according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of vibration acceleration signals of an outer ring fault bearing according to an embodiment of the present invention;
FIG. 3 is a graph showing the evaluation result of the bearing fault characteristic information using a 1/3-binary tree filter bank according to the embodiment of the present invention;
FIG. 4 is an optimal product envelope spectrum of a bandpass filtered signal according to an embodiment of the invention;
Detailed Description
In order to more clearly describe the technical scheme of the embodiment of the present invention, the embodiment of the present invention will be described in detail below with reference to the accompanying drawings.
Example 1
As shown in fig. 1:
the rotary machine fault diagnosis method based on the product envelope spectrum comprises the following steps:
s1: designing a band-pass filter group to carry out band-pass filtering on the collected vibration acceleration signals to obtain a group of band-pass filtering signals;
in this embodiment, S1 specifically includes:
s101: performing mean value removal pretreatment on vibration acceleration signals acquired by a sensor arranged on the rotary mechanical equipment;
s102: and designing a band-pass filter group to carry out band-pass filtering on the vibration acceleration signals after the mean value is removed, so as to obtain a group of filtering signals with different center frequencies and bandwidths.
In this embodiment, the filter bank of S102 is a 1/3-binary tree filter bank, a sliding window filter bank, a wavelet packet transform filter bank, an empirical wavelet transform filter bank, or other filter banks.
S2: selecting a plurality of proper exponentials, constructing a product envelope spectrum by a generalized envelope spectrum, and then calculating the product envelope spectrum of each band-pass filtering signal;
in this embodiment, S2 specifically includes:
s201: based on the hilbert transform, an analytic signal of each band-pass filtered signal is constructed as follows:
where a (t) is an analysis signal of the band-pass filtered signal, x bp,k (t) is the kth bandpass filtered signal of the measurement signal x (t), H { · } is the Hilbert transform, j is the imaginary unit, t is time, τ is the integral variable;
s202: selecting proper power exponent p, based on simplified Box-Cox transformation, constructing generalized envelope signal v from analytic signal of band-pass filtering signal p (t); generalized envelope signal v p The calculation formula of (t) is as follows:
s203: based on the fast Fourier transform, a corresponding generalized envelope spectrum is obtained by calculation of a generalized envelope signal, and the calculation formula is as follows:
wherein S is p (f) A generalized envelope spectrum with a power exponent of p, wherein F { · } is a fast Fourier transform, and F is a frequency;
s204: selecting M suitable exponentiations, i.e. p 1 ,p 2 ,...,p M The M generalized envelope spectrums of each band-pass filtered signal are constructed to obtain corresponding product envelope spectrums, and the calculation formula is as follows:
wherein S is PES (f) For the product envelope spectrum,
and->
Respectively the exponentiation of power of p m Generalized envelope and generalized envelope spectrum, p, of a time-bandpass filtered signal m Is the mth power exponent. />
S3: selecting theoretical fault characteristic frequency and calculating a frequency domain signal-to-noise ratio index of a product envelope spectrum of each band-pass filtering signal to obtain fault characteristic information quantization evaluation results of different spectrum bands;
in this embodiment, S3 specifically includes:
s301: estimating a theoretical value of the fault characteristic frequency according to the size parameter and the rotating speed of the rotating mechanical parts;
s302: calculating the frequency domain signal-to-noise ratio index of the product envelope spectrum of each band-pass filtering signal, and estimating the richness of fault characteristic information of different spectrum bands; the calculation formula of the frequency domain signal to noise ratio is as follows:
wherein I is FDSNR For the frequency domain signal-to-noise ratio, H is the number of harmonics of the fault signature frequency of interest; omega shape h Representing a frequency f of failure characteristics m Is centered on the h harmonic of (a) and comprises the frequency hf m And a plurality of frequency components on both sides thereof; s is S PES (α) is the magnitude of the product envelope spectrum at frequency α; f (f) l For the first spectral frequency; s is S PES (f l ) Envelope the spectrum at frequency f for the product l Amplitude at; l is the number of spectral frequencies of interest.
Further, the maximum value f of the spectral band range of the product envelope spectrum involved in the calculation L Should be at least greater than the fault signature frequency f m 3 times of (3).
S4: and analyzing the product envelope spectrum of the band-pass filtered signal corresponding to the maximum frequency domain signal-to-noise ratio index, and diagnosing the rotating machinery fault.
In this embodiment, S4 specifically includes:
s401: comparing the frequency domain signal-to-noise ratio indexes of the filtering signals of different spectrum bands, and identifying the product envelope spectrum of the band-pass filtering signals corresponding to the maximum value of the frequency domain signal-to-noise ratio indexes, namely the optimal product envelope spectrum;
s402: and analyzing frequency components in the optimal product envelope spectrum, and diagnosing faults of the rotary machine according to the fault characteristic frequency.
Example 2
Based on the rotary machine fault diagnosis method based on the product envelope spectrum described in embodiment 1, the present embodiment uses a vibration acceleration signal of the axle box bearing of the railway passenger car, which is obtained by a vibration acceleration sensor and actually measures the fault of the outer ring, to clarify the implementation flow and the diagnosis effect of the present invention;
fig. 2 is a vibration acceleration signal of a passenger train axlebox bearing with an outer ring failure. And carrying out band-pass filtering on the measured vibration acceleration signal by adopting a 1/3-binary tree structure filter bank, and setting the decomposition layer number of the spectrum band to be 4. The power exponentials of the generalized envelope spectrum used to construct the product envelope spectrum are set to 0.5, 0.8, 1 and 2. Setting the maximum value f of the frequency band range of the product envelope spectrum participating in the calculation when calculating the frequency domain signal-to-noise ratio index of the product envelope spectrum of the band-pass filtered signal L Is the fault characteristic frequency f m 3.5 times of (3).
FIG. 3 is a graph showing the evaluation result of bearing fault characteristic information of different spectral bands when a 1/3 binary tree filter bank is used for bandpass filtering. According to fig. 3, the center frequency and bandwidth of the spectral band corresponding to the maximum frequency domain signal-to-noise ratio index are 2800Hz and 800Hz, respectively.
Fig. 4 shows the product envelope spectrum of the band-pass filtered signal of the spectrum band corresponding to the maximum frequency domain signal-to-noise ratio index, i.e. the optimal product envelope spectrum. In FIG. 4, the bearing outer race failure characteristic frequency f can be clearly observed o Harmonic 2f of o And 3f o The spectrum line of the bearing shows that the outer ring of the bearing has faults, and the method provided by the invention effectively diagnoses the faults of the bearing. These results strongly demonstrate the effectiveness of the proposed method in the diagnosis of faults in rotating machinery.
In summary, the rotating machinery fault diagnosis method based on the product envelope spectrum has the characteristic that the generalized envelope spectrum with different power indexes shows different performance characteristics to different interference noises; meanwhile, the product envelope spectrum combines the advantages of different generalized envelope spectrums, so that not only can the periodic modulation component in the signal be enhanced, but also the non-periodic interference noise component in the signal can be eliminated, and the identification of the fault characteristic frequency and the harmonic wave thereof is facilitated; the fault characteristic frequency in the product envelope spectrum of the measurement signal after band-pass filtering is more remarkable, and the accuracy of mechanical fault diagnosis under different interference noise pollution can be effectively improved.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (7)
1. The rotary machine fault diagnosis method based on the product envelope spectrum is characterized by comprising the following steps of:
s1: designing a band-pass filter group to carry out band-pass filtering on the collected vibration acceleration signals to obtain a group of band-pass filtering signals;
s2: selecting a plurality of proper exponentials, constructing a product envelope spectrum by a generalized envelope spectrum, and then calculating the product envelope spectrum of each band-pass filtering signal;
s3: selecting theoretical fault characteristic frequency and calculating a frequency domain signal-to-noise ratio index of a product envelope spectrum of each band-pass filtering signal to obtain fault characteristic information quantization evaluation results of different spectrum bands;
s4: and analyzing the product envelope spectrum of the band-pass filtered signal corresponding to the maximum frequency domain signal-to-noise ratio index, and diagnosing the rotating machinery fault.
2. The rotary machine fault diagnosis method based on product envelope spectrum according to claim 1, wherein S1 specifically comprises:
s101: performing mean value removal pretreatment on vibration acceleration signals acquired by a sensor arranged on the rotary mechanical equipment;
s102: and designing a band-pass filter group to carry out band-pass filtering on the vibration acceleration signals after the mean value is removed, so as to obtain a group of filtering signals with different center frequencies and bandwidths.
3. The method for diagnosing a rotary machine fault based on a product envelope spectrum as claimed in claim 2, wherein the filter bank of S102 is a 1/3-binary tree structure filter bank, a sliding window filter bank, a wavelet packet transform filter bank, an empirical wavelet transform filter bank or other forms of filter banks.
4. The rotary machine fault diagnosis method based on product envelope spectrum according to claim 1, wherein S2 specifically comprises:
s201: based on the hilbert transform, an analytic signal of each band-pass filtered signal is constructed as follows:
where a (t) is an analysis signal of the band-pass filtered signal, x bp,k (t) is the kth bandpass filtered signal of the measurement signal x (t), H { · } is the Hilbert transform, j is the imaginary unit, t is time, τ is the integral variable;
s202: selecting a proper power exponent p, and constructing a generalized envelope signal by an analytic signal of a band-pass filtering signal based on simplified Box-Cox conversion; the generalized envelope signal is calculated as follows:
s203: based on the fast Fourier transform, a corresponding generalized envelope spectrum is obtained by calculation of a generalized envelope signal, and the calculation formula is as follows:
wherein S is p (f) A generalized envelope spectrum with a power exponent of p, wherein F { · } is a fast Fourier transform, and F is a frequency;
s204: selecting M suitable exponentiations, i.e. p 1 ,p 2 ,...,p M The M generalized envelope spectrums of each band-pass filtered signal are constructed to obtain corresponding product envelope spectrums, and the calculation formula is as follows:
wherein S is PES (f) For the product envelope spectrum,
and->
Respectively the exponentiation of power of p m Generalized envelope and generalized envelope spectrum, p, of a time-bandpass filtered signal m Is the mth power exponent.
5. The rotary machine fault diagnosis method based on product envelope spectrum according to claim 1, wherein S3 specifically comprises:
s301: estimating a theoretical value of the fault characteristic frequency according to the size parameter and the rotating speed of the rotating mechanical parts;
s302: calculating the frequency domain signal-to-noise ratio index of the product envelope spectrum of each band-pass filtering signal, and estimating the richness of fault characteristic information of different spectrum bands; the calculation formula of the frequency domain signal to noise ratio is as follows:
wherein I is FDSNR For the frequency domain signal-to-noise ratio, H is the number of harmonics of the fault signature frequency of interest; omega shape h Representing a frequency f of failure m Is centered on the h harmonic of (a) and comprises the frequency hf m And a plurality of frequency components on both sides thereof; s is S PES (α) is the magnitude of the product envelope spectrum at frequency α; f (f) l For the first spectral frequency; s is S PES (f l ) Envelope the spectrum at frequency f for the product l Amplitude at; l is the number of spectral frequencies of interest.
6. The rotary machine fault diagnosis method based on product envelope spectrum as claimed in claim 5, wherein: maximum f of spectral band range of product envelope spectrum involved in calculation L Should be at least greater than the failure characteristic frequency f m 3 times of (3).
7. The rotary machine fault diagnosis method based on product envelope spectrum according to claim 1, wherein S4 specifically comprises:
s401: comparing the frequency domain signal-to-noise ratio indexes of the filtering signals of different spectrum bands, and identifying the product envelope spectrum of the band-pass filtering signals corresponding to the maximum value of the frequency domain signal-to-noise ratio indexes, namely the optimal product envelope spectrum;
s402: and analyzing frequency components in the optimal product envelope spectrum, and diagnosing faults of the rotary machine according to the fault characteristic frequency.
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| CN117723303B (en) * | 2024-02-01 | 2024-05-10 | 湘潭大学 | Acoustic monitoring method for wind generating set bearing |
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