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:
in the formula (I), the compound is shown in the specification,
for unbiased autocorrelation of the filtered signal y, N is the signal length, τ is the delay coefficient, t
iIs time, q ═ 0., N-1, said τ ═ q/f
s,f
sIs 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:
in the formula: < > denotes the time domain averaging operator.
Further, in step S5, the new filter coefficient is obtained by the following formula:
in the formula, X0Is a matrix.
Further, in the formula for obtaining the new filter coefficient in
step 5, the matrix
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:
in the formula (I), the compound is shown in the specification,
is the filter coefficient estimate, f is the filter,
is composed of
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.
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
TABLE 2 proportionality coefficient of fan end bearing failure characteristic frequency to shaft rotation frequency
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:
in the formula (I), the compound is shown in the specification,
for unbiased autocorrelation of the filtered signal y, N is the signal length, τ is the delay coefficient, t
iIs time, q ═ 0., N-1, said τ ═ q/f
s,f
sIs 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:
in the formula: < > denotes the time domain averaging operator.
Further, in step S5, the new filter coefficient is obtained by the following formula:
in the formula, X0Is a matrix.
Further, in the formula for obtaining the new filter coefficient in
step 5, the matrix
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:
in the formula (I), the compound is shown in the specification,
is the filter coefficient estimate, f is the filter,
is composed of
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.