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

本发明公开了基于一种增强最小熵解卷积的机械故障诊断方法及系统，涉及信号处理与机械故障诊断技术领域，包括以下步骤：S1，输入测量信号，随机初始化滤波器系数；S2，求解滤波信号；S3，对滤波信号进行无偏自相关变换，并将变换后的信号作为新的滤波信号；S4，计算滤波信号的峭度；S5，更新滤波器系数，获得新的滤波器；S6，重复步骤S2‑S5，使得滤波信号峭度达到最大；S7，选择滤波信号峭度达到最大时对应的滤波器作为最优滤波器，对应的经无偏自相关变换后的信号作为最终滤波信号；S8，对滤波信号进行时域分析和包络分析，并根据分析结果诊断轴承故障。

Figure 202110797214

The invention discloses a mechanical fault diagnosis method and system based on enhanced minimum entropy deconvolution, and relates to the technical field of signal processing and mechanical fault diagnosis. Filter the signal; S3, perform unbiased autocorrelation transformation on the filtered signal, and use the transformed signal as a new filter signal; S4, calculate the kurtosis of the filtered signal; S5, update the filter coefficient to obtain a new filter; S6 , repeat steps S2-S5, make the filter signal kurtosis reach the maximum; S7, select the filter corresponding to the filter signal when the kurtosis reaches the maximum as the optimal filter, and the corresponding signal after unbiased autocorrelation transformation is used as the final filter signal ; S8, perform time domain analysis and envelope analysis on the filtered signal, and diagnose bearing faults according to the analysis results.

Figure 202110797214

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.基于一种增强最小熵解卷积的机械故障诊断方法及系统，其特征在于，包括以下步骤：1. a mechanical fault diagnosis method and system based on enhanced minimum entropy deconvolution, is characterized in that, comprises the following steps: S1，输入测量信号，随机初始化滤波器系数，执行步骤S2；S1, input the measurement signal, initialize the filter coefficient randomly, and execute step S2; S2，求解滤波信号，执行步骤S3；S2, solve the filtered signal, and execute step S3; S3，对滤波信号进行无偏自相关变换，并将变换后的信号作为新的滤波信号，执行步骤S4；S3, perform unbiased autocorrelation transformation on the filtered signal, and use the transformed signal as a new filtered signal, and execute step S4; S4，计算滤波信号的峭度，执行步骤S5；S4, calculate the kurtosis of the filtered signal, and execute step S5; S5，更新滤波器系数，获得新的滤波器，执行步骤S6；S5, update the filter coefficients to obtain a new filter, and execute step S6; S6，重复步骤S2-S5，使得滤波信号的峭度达到最大，执行步骤S7；S6, repeat steps S2-S5, so that the kurtosis of the filtered signal reaches the maximum, and execute step S7; S7，选择滤波信号峭度达到最大时对应的滤波器作为最优滤波器，对应的经无偏自相关变换后的信号作为最终滤波信号，执行步骤S8；S7, select the filter corresponding to when the kurtosis of the filtered signal reaches the maximum as the optimal filter, and the corresponding signal after unbiased autocorrelation transformation as the final filtered signal, and perform step S8; S8，对滤波信号进行时域分析和包络分析，并根据分析结果诊断轴承故障。S8, performing time domain analysis and envelope analysis on the filtered signal, and diagnosing bearing faults according to the analysis results. 2.根据权利要求1所述的基于一种增强最小熵解卷积的机械故障诊断方法及系统，其特征在于，所述步骤S3中，对滤波信号进行无偏自相关变换的公式为：2. the mechanical fault diagnosis method and system based on a kind of enhanced minimum entropy deconvolution according to claim 1, is characterized in that, in described step S3, the formula that filter signal is carried out unbiased autocorrelation transformation is:
Figure FDA0003163270420000011
Figure FDA0003163270420000011
 式中，
Figure FDA0003163270420000012
为滤波信号y的无偏自相关，N为信号长度，τ为延迟系数，ti为时间，q＝0,...,N-1，所述τ＝q/fs，fs为采样频率。In the formula,
Figure FDA0003163270420000012
is the unbiased autocorrelation of the filtered signal y, N is the signal length, τ is the delay coefficient, t i is the time, q=0,...,N-1, the τ=q/f s , f s is the sampling frequency. 3.根据权利要求1所述的基于一种增强最小熵解卷积的机械故障诊断方法及系统，其特征在于，所述步骤S4中，计算进行无偏自相关分析后滤波信号的峭度的表达公式为：3. the mechanical fault diagnosis method and system based on a kind of enhanced minimum entropy deconvolution according to claim 1, it is characterized in that, in described step S4, calculate the kurtosis of filter signal after carrying out unbiased autocorrelation analysis. The expression formula is:
Figure FDA0003163270420000013
Figure FDA0003163270420000013
 式中：<·>表示时域平均算子。In the formula: <·> represents the time domain averaging operator. 4.根据权利要求1所述的基于一种增强最小熵解卷积的机械故障诊断方法及系统，所述步骤S6中重复步骤S2-S5，使得滤波信号进行无偏自相关变换后的峭度达到最大，选择最大峭度对应的滤波器参数，描述此过程公式如下：4. the mechanical fault diagnosis method and system based on a kind of enhanced minimum entropy deconvolution according to claim 1, repeat steps S2-S5 in described step S6, make the kurtosis after unbiased autocorrelation transformation of the filtered signal To reach the maximum, select the filter parameters corresponding to the maximum kurtosis, and the formula to describe this process is as follows:
Figure FDA0003163270420000014
Figure FDA0003163270420000014
 式中，
Figure FDA0003163270420000015
为滤波器系数估计值，f为滤波器，
Figure FDA0003163270420000016
为
Figure FDA0003163270420000017
的峭度。In the formula,
Figure FDA0003163270420000015
is the estimated value of the filter coefficient, f is the filter,
Figure FDA0003163270420000016
for
Figure FDA0003163270420000017
kurtosis. 5.根据权利要求1所述的基于一种增强最小熵解卷积的机械故障诊断方法及系统，其特征在于，所述步骤S5中，新的滤波器的系数获得的公式为：5. the mechanical fault diagnosis method and system based on a kind of enhanced minimum entropy deconvolution according to claim 1, is characterized in that, in described step S5, the formula that the coefficient of new filter obtains is:
Figure FDA0003163270420000021
Figure FDA0003163270420000021
 式中，X0为一矩阵。In the formula, X 0 is a matrix. 6.根据权利要求5所述的基于一种增强最小熵解卷积的机械故障诊断方法及系统，其特征在于，所述更新滤波器的系数获得的公式中，所述矩阵
Figure FDA0003163270420000022
式中L为滤波器长度。6 . The method and system for diagnosing mechanical faults based on enhanced minimum entropy deconvolution according to claim 5 , wherein, in the formula obtained by the coefficients of the updated filter, the matrix
Figure FDA0003163270420000022
where L is the filter length. 7.根据权利要求1所述的基于一种增强最小熵解卷积的机械故障诊断方法及系统，其特征在于，所述测量信号的卷积形式为：7. the mechanical fault diagnosis method and system based on a kind of enhanced minimum entropy deconvolution according to claim 1, is characterized in that, the convolution form of described measurement signal is: x＝e*he+n*hn x=e*h e +n*h n 式中，*代表卷积运算，x为测量信号，e为周期性故障信号，n为干扰成分，he、hn分别为e和n对应的传递函数。In the formula, * represents the convolution operation, x is the measurement signal, e is the periodic fault signal, n is the interference component, and h e and h n are the transfer functions corresponding to e and n, respectively. 8.根据权利要求7所述的基于一种增强最小熵解卷积的机械故障诊断方法及系统，其特征在于，所述干扰成分包括:杂乱冲击干扰、非高斯谐波分量、高斯白噪声。8. the mechanical fault diagnosis method and system based on a kind of enhanced minimum entropy deconvolution according to claim 7, is characterized in that, described disturbance component comprises: random impact disturbance, non-Gaussian harmonic component, Gaussian white noise.
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陈海周等: "基于最小熵解卷积和Teager能量算子直升机滚动轴承复合故障诊断研究", 《振动与冲击》 *

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CN114528525A (en) * 2022-01-11 2022-05-24 西南交通大学 Mechanical fault diagnosis method based on maximum weighted kurtosis blind deconvolution
CN114548150A (en) * 2022-01-11 2022-05-27 西南交通大学 Fault Diagnosis Method of Transmission System Based on Parameter Adaptive Enhanced MED
CN114528525B (en) * 2022-01-11 2023-03-28 西南交通大学 Mechanical fault diagnosis method based on maximum weighted kurtosis blind deconvolution
CN115931358A (en) * 2023-02-24 2023-04-07 沈阳工业大学 A low signal-to-noise ratio acoustic emission signal diagnosis method for bearing faults
CN115931358B (en) * 2023-02-24 2023-09-12 沈阳工业大学 Bearing fault acoustic emission signal diagnosis method with low signal-to-noise ratio
CN118690211A (en) * 2024-05-31 2024-09-24 兰州理工大学 Rolling bearing composite fault diagnosis method based on improved symplectic geometric mode decomposition
CN118690211B (en) * 2024-05-31 2025-07-04 兰州理工大学 Rolling bearing composite fault diagnosis method based on improved symplectic geometric mode decomposition

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