CN113702042A - Mechanical fault diagnosis method and system based on enhanced minimum entropy deconvolution - Google Patents

Mechanical fault diagnosis method and system based on enhanced minimum entropy deconvolution Download PDF

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CN113702042A
CN113702042A CN202110797214.6A CN202110797214A CN113702042A CN 113702042 A CN113702042 A CN 113702042A CN 202110797214 A CN202110797214 A CN 202110797214A CN 113702042 A CN113702042 A CN 113702042A
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张新
赵艺珂
王家序
吴磊
张忠强
何劲峰
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Southwest Jiaotong University
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Abstract

The invention discloses a mechanical fault diagnosis method and a system based on enhanced minimum entropy deconvolution, which relate to the technical field of signal processing and mechanical fault diagnosis and comprise the following steps: s1, inputting a measuring signal, and randomly initializing a filter coefficient; s2, solving a filtering signal; s3, carrying out unbiased autocorrelation transformation on the filtered signal, and taking the transformed signal as a new filtered signal; s4, calculating the kurtosis of the filtering signal; s5, updating the filter coefficient to obtain a new filter; s6, repeating the steps S2-S5 to make the kurtosis of the filtering signal reach the maximum; s7, selecting the filter corresponding to the maximum kurtosis of the filtering signal as the optimal filter, and taking the corresponding signal after unbiased autocorrelation transformation as the final filtering signal; and S8, performing time domain analysis and envelope analysis on the filtered signal, and diagnosing the bearing fault according to the analysis result.

Description

Mechanical fault diagnosis method and system based on enhanced minimum entropy deconvolution
Technical Field
The invention relates to the technical field of mechanical fault diagnosis, in particular to a mechanical fault diagnosis method and system based on enhanced minimum entropy deconvolution.
Background
In the rotating machinery health monitoring and fault diagnosis based on vibration signal analysis, due to the existence of a plurality of vibration sources and the influence of complex transmission paths and strong noise, the measured vibration signals are usually complex in components and may contain non-gaussian interference components such as gaussian white noise, harmonic components, strong impact interference and the like, so that periodic fault signals are very weak.
According to the scheme, unbiased autocorrelation analysis is integrated into iterative solution of a minimum entropy deconvolution filter coefficient, and an enhanced minimum entropy deconvolution method based on the unbiased autocorrelation analysis is provided, so that the problem that the periodic fault impact of a bearing cannot be effectively recovered due to overlarge kurtosis caused by dominant impact interference in the traditional minimum entropy deconvolution is solved. The method can accurately recover the periodic fault impact sequence of the bearing under the interference of strong impact, white Gaussian noise, harmonic component and the like, and extract fault characteristic information.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a mechanical fault diagnosis method and system based on enhanced minimum entropy deconvolution.
The purpose of the invention is realized by the following technical scheme:
a mechanical fault diagnosis method and system based on enhanced minimum entropy deconvolution includes the following steps:
s1, inputting the measuring signal, initializing the filter coefficient randomly, and executing the step S2;
s2, solving the filtering signal, and executing the step S3;
s3, performing unbiased autocorrelation transform on the filtered signal, and performing step S4 using the transformed signal as a new filtered signal;
s4, calculating the kurtosis of the filtering signal, and executing the step S5;
s5, updating the filter coefficient to obtain a new filter, and executing the step S6;
s6, repeating the steps S2-S5 to make the kurtosis of the filtering signal reach the maximum, and executing the step S7;
s7, selecting the filter corresponding to the maximum kurtosis of the filtering signal as the optimal filter, and the corresponding signal after unbiased autocorrelation transformation as the final filtering signal, and executing the step S8;
and S8, performing time domain analysis and envelope analysis on the filtered signal, and diagnosing the bearing fault according to the analysis result. .
Further, in step S3, the formula for performing unbiased autocorrelation transform on the filtered signal is as follows:
Figure BDA0003163270430000011
in the formula (I), the compound is shown in the specification,
Figure BDA0003163270430000021
for unbiased autocorrelation of the filtered signal y, N is the signal length, τ is the delay coefficient, tiIs time, q ═ 0., N-1, said τ ═ q/fs，fsIs the sampling frequency.
Further, in the step S4, according to
The expression formula of the kurtosis of the filtering signal after the unbiased autocorrelation analysis is as follows:
Figure BDA0003163270430000022
in the formula: < > denotes the time domain averaging operator.
Further, in step S5, the new filter coefficient is obtained by the following formula:
Figure BDA0003163270430000023
in the formula, X0Is a matrix.
Further, in the formula for obtaining the new filter coefficient in step 5, the matrix
Figure BDA0003163270430000024
Further, in the step S6, steps S2-S5 are repeated, so that the kurtosis of the filtered signal after the unbiased autocorrelation transformation is maximized, and the filter parameter corresponding to the maximum kurtosis is selected, which describes the following process formula:
Figure BDA0003163270430000025
in the formula (I), the compound is shown in the specification,
Figure BDA0003163270430000026
is the filter coefficient estimate, f is the filter,
Figure BDA0003163270430000027
is composed of
Figure BDA0003163270430000028
The kurtosis of (c).
Further, the convolution form of the measurement signal is:
x＝e*he+n*hn
wherein x represents convolution operation, x is measurement signal, e is periodic fault signal, n is interference component, he、hnThe transfer functions for e and n, respectively.
Further, the interference components comprise clutter impact interference, non-Gaussian harmonic components and Gaussian white noise.
The invention has the beneficial effects that:
according to the scheme, unbiased autocorrelation analysis is integrated into iterative solution of a minimum entropy deconvolution filter coefficient, and an enhanced minimum entropy deconvolution method based on the unbiased autocorrelation analysis is provided, so that the problem that the periodic fault impact of a bearing cannot be effectively recovered due to overlarge kurtosis caused by dominant impact interference in the traditional minimum entropy deconvolution is solved. The method can accurately recover the periodic fault impact sequence of the bearing under the interference of strong impact, white Gaussian noise, harmonic component and the like, and extract fault characteristic information.
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FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a time domain oscillogram and an envelope spectrogram (a-time domain oscillogram, b-envelope spectrogram) of a measuring signal of a fault bearing of the invention;
FIG. 3 is a filtered signal diagram of a fault bearing measurement signal obtained by the method of the present invention, the minimum entropy deconvolution method, the MEDA method, and the OMEDA method, respectively (a-the method of the present invention, b-the minimum entropy deconvolution method, c-the MEDA method, and d-the OMEDA method);
FIG. 4 shows the envelope spectrograms of the filtered signal corresponding to the method of the present invention, the minimum entropy deconvolution method, the MEDA method, and the OMEDA method, respectively (a-the method of the present invention, b-the minimum entropy deconvolution method, c-the MEDA method, and d-the OMEDA method).
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to fig. 1 to 4 of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments, and based on the embodiments of the present invention, a person skilled in the art can obtain all other embodiments without creative efforts.
In the description of the present invention, it is to be understood that the terms "counterclockwise", "clockwise", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate orientations or positional relationships based on those shown in the drawings, and are used for convenience of description only, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be considered as limiting.
A mechanical fault diagnosis method and system based on enhanced minimum entropy deconvolution includes the following steps:
s1, inputting the measuring signal, initializing the filter coefficient randomly, and executing the step S2;
s2, solving the filtering signal, and executing the step S3;
s3, performing unbiased autocorrelation transform on the filtered signal, and performing step S4 using the transformed signal as a new filtered signal;
s4, calculating the kurtosis of the filtering signal, and executing the step S5;
s5, updating the filter coefficient to obtain a new filter, and executing the step S6;
s6, repeating the steps S2-S5 to make the kurtosis of the filtering signal reach the maximum, and executing the step S7;
s7, selecting the filter corresponding to the maximum kurtosis of the filtering signal as the optimal filter, and the corresponding signal after unbiased autocorrelation transformation as the final filtering signal, and executing the step S8;
and S8, performing time domain analysis and envelope analysis on the filtered signal, and diagnosing the bearing fault according to the analysis result.
Example (b):
in the embodiment, a group of signals in a bearing test data set of the university of Kaiser storage (CWRU) is selected for analysis, and the bearing test device comprises a fan end bearing (6203-2RS JEM SKF), a driving motor, a driving end bearing (6205-2RS JEM SKF), a torque sensor, a load motor and the like. The test bearing is a fan end bearing (specific parameters are shown in table 1), partial faults are implanted into a rolling body of the test bearing by adopting electric spark machining, and the proportionality coefficient of fault characteristic frequency of each part (inner ring, outer ring, retainer and rolling body) of the bearing relative to the rotating frequency of the rotating shaft is shown in table 2. The sampling frequency of the signal is 12kHz, and the rotating shaft frequency is frThe fault characteristic frequency f of the bearing rolling body is calculated according to the proportionality coefficient in the table 2 and is 29.53HzbAs shown in formula:
fb＝fr×1.994＝58.883Hz
TABLE 1 Fan end bearing dimensional parameters in inches
Figure BDA0003163270430000041
TABLE 2 proportionality coefficient of fan end bearing failure characteristic frequency to shaft rotation frequency
Figure BDA0003163270430000042
Fig. 2 shows a time domain waveform and an envelope spectrum of a signal, and it can be seen that the time domain waveform contains obvious strong impact interference, and the envelope spectrum cannot identify fault characteristic frequency.
Fig. 3 and 4 show the filtered signals and the corresponding envelope spectra of the proposed method and the comparison method, respectively. Therefore, the provided method effectively recovers periodic fault impact, and the characteristic frequency f of the bearing fault can be accurately identified in the envelope spectrumbAnd frequency multiplication (2 f) thereofb、3fb、4fb) While also observing the frequency of the turn-around frThe diagnosis result is in accordance with the actual situation. The three comparison methods cannot effectively extract the fault characteristics of the bearing, only a small amount of pseudo leading impact is extracted, and the analysis results show that compared with the three traditional methods, the method has the advantages of obviously improving the actual signal analysis effect and having great advantages in the aspects of impact interference suppression and fault impact enhancement.
Further, in step S3, the formula for performing unbiased autocorrelation transform on the filtered signal is as follows:
Figure BDA0003163270430000051
in the formula (I), the compound is shown in the specification,
Figure BDA0003163270430000052
for unbiased autocorrelation of the filtered signal y, N is the signal length, τ is the delay coefficient, tiIs time, q ═ 0., N-1, said τ ═ q/fs，fsIs the sampling frequency.
Further, in the step S4, according to
The expression formula of the kurtosis of the filtering signal after the unbiased autocorrelation analysis is as follows:
Figure BDA0003163270430000053
in the formula: < > denotes the time domain averaging operator.
Further, in step S5, the new filter coefficient is obtained by the following formula:
Figure BDA0003163270430000054
in the formula, X0Is a matrix.
Further, in the formula for obtaining the new filter coefficient in step 5, the matrix
Figure BDA0003163270430000055
Further, in the step S6, steps S2-S5 are repeated, so that the kurtosis of the filtered signal after the unbiased autocorrelation transformation is maximized, and the filter parameter corresponding to the maximum kurtosis is selected, which describes the following process formula:
Figure BDA0003163270430000056
in the formula (I), the compound is shown in the specification,
Figure BDA0003163270430000057
is the filter coefficient estimate, f is the filter,
Figure BDA0003163270430000058
is composed of
Figure BDA0003163270430000059
The kurtosis of (c).
Further, the convolution form of the measurement signal is:
x＝e*he+n*hn
wherein, x represents convolution operation, x is measurement signal, and e is periodic fault signalN is an interference component, he、hnThe transfer functions for e and n, respectively.
Further, the interference components comprise clutter impact interference, non-Gaussian harmonic components and Gaussian white noise.
The foregoing is merely a preferred embodiment of the invention, it being understood that the embodiments described are part of the invention, and not all of it. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention. The invention is not intended to be limited to the forms disclosed herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (8)

1. The method and the system for diagnosing the mechanical fault based on the enhanced minimum entropy deconvolution are characterized by comprising the following steps of:
s1, inputting the measuring signal, initializing the filter coefficient randomly, and executing the step S2;
s2, solving the filtering signal, and executing the step S3;
s3, performing unbiased autocorrelation transform on the filtered signal, and performing step S4 using the transformed signal as a new filtered signal;
s4, calculating the kurtosis of the filtering signal, and executing the step S5;
s5, updating the filter coefficient to obtain a new filter, and executing the step S6;
s6, repeating the steps S2-S5 to make the kurtosis of the filtering signal reach the maximum, and executing the step S7;
s7, selecting the filter corresponding to the maximum kurtosis of the filtering signal as the optimal filter, and the corresponding signal after unbiased autocorrelation transformation as the final filtering signal, and executing the step S8;
and S8, performing time domain analysis and envelope analysis on the filtered signal, and diagnosing the bearing fault according to the analysis result.
2. The method and system for diagnosing mechanical failure based on enhanced minimum entropy deconvolution of claim 1, wherein in step S3, the formula for performing unbiased autocorrelation transformation on the filtered signal is:
Figure FDA0003163270420000011
in the formula (I), the compound is shown in the specification,
Figure FDA0003163270420000012
for unbiased autocorrelation of the filtered signal y, N is the signal length, τ is the delay coefficient, tiIs time, q ═ 0., N-1, said τ ═ q/fs，fsIs the sampling frequency.
3. The method and system for diagnosing mechanical failure based on enhanced minimum entropy deconvolution of claim 1, wherein in step S4, the expression formula for calculating the kurtosis of the filtered signal after unbiased autocorrelation analysis is:
Figure FDA0003163270420000013
in the formula: < > denotes the time domain averaging operator.
4. The method and system for diagnosing mechanical failure based on an enhanced minimum entropy deconvolution as claimed in claim 1, wherein the steps S2-S5 are repeated in the step S6 to maximize the kurtosis of the filtered signal after unbiased autocorrelation transformation, and the filter parameters corresponding to the maximum kurtosis are selected, which is described as follows:
Figure FDA0003163270420000014
in the formula (I), the compound is shown in the specification,
Figure FDA0003163270420000015
is the filter coefficient estimate, f is the filter,
Figure FDA0003163270420000016
is composed of
Figure FDA0003163270420000017
The kurtosis of (c).
5. The method and system for diagnosing mechanical failure based on an enhanced minimum entropy deconvolution as claimed in claim 1, wherein in step S5, the new filter coefficients are obtained by the following formula:
Figure FDA0003163270420000021
in the formula, X0Is a matrix.
6. The method and system for diagnosing mechanical failure based on an enhanced minimum entropy deconvolution as claimed in claim 5, wherein the matrix is obtained from the formula obtained by updating the coefficients of the filter
Figure FDA0003163270420000022
Where L is the filter length.
7. The method and system for diagnosing mechanical failure based on enhanced minimum entropy deconvolution of claim 1, wherein the convolution form of the measurement signal is:
x＝e*he+n*hn
wherein, represents convolution operation, and x is measurement signalNumber e is a periodic fault signal, n is an interference component, he、hnThe transfer functions for e and n, respectively.
8. The method and system for diagnosing mechanical failure based on enhanced minimum entropy deconvolution of claim 7, wherein the interference components include clutter and interference, non-Gaussian harmonic components, and Gaussian white noise.
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