Gearbox compound fault diagnosis based on a combined MSGMD-MOMEDA method

Author(s):  
Jianqun Zhang ◽  
Qing Zhang ◽  
Xianrong Qin ◽  
Yuantao Sun ◽  
Jun Zhang

Abstract Weak fault detection is a complex and challenging task when two or more faults (compound fault) with discordant severity occur in different parts of a gearbox. The weak fault features are prone to be submerged by the severe fault features and strong background noise, which easily lead to a missed diagnosis. To solve this problem, a novel diagnosis method combining muti-symplectic geometry mode decomposition and multipoint optimal minimum entropy deconvolution adjusted (MSGMD-MOMEDA) is proposed for gearbox compound fault in this paper. Specifically, different fault components are separated by the improved symplectic geometry mode decomposition (SGMD), namely, multi-SGMD (MSGMD) method. The weak fault features are enhanced by the multipoint optimal minimum entropy deconvolution adjusted (MOMEDA). In the process of research, a new scheme of selecting key parameters of MOMEDA is proposed, which is a key step in applying MOMEDA. Compared with SGMD, the proposed MSGMD has two main improvements, including suppressing mode mixing and preventing the generation of the pseudo components. Compared with the original method of selecting parameters based on multipoint kurtosis, the proposed MOMEDA parameters selecting scheme has more merits of high accuracy and precision. The analysis results of two cases of simulation and experiment signal reveal that the MSGMD-MOMEDA method can accurately diagnose the gearbox compound fault even under strong background noise.

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1106
Author(s):  
Wenhua Du ◽  
Xiaoming Guo ◽  
Xiaofeng Han ◽  
Junyuan Wang ◽  
Jie Zhou ◽  
...  

Minimum entropy deconvolution (MED) is not effective in extracting fault features in strong noise environments, which can easily lead to misdiagnosis. Moreover, the noise reduction effect of MED is affected by the size of the filter. In the face of different vibration signals, the size of the filter is not adaptive. In order to improve the efficiency of MED fault feature extraction, this paper proposes a firefly optimization algorithm (FA) to improve the MED fault diagnosis method. Firstly, the original vibration signal is stratified by white noise-assisted singular spectral decomposition (SSD), and the stratified signal components are divided into residual signal components and noisy signal components by a detrended fluctuation analysis (DFA) algorithm. Then, the noisy components are preprocessed by an autoregressive (AR) model. Secondly, the envelope spectral entropy is proposed as the fitness function of the FA algorithm, and the filter size of MED is optimized by the FA algorithm. Finally, the preprocessed signal is denoised and the pulse enhanced with the proposed adaptive MED. The new method is validated by simulation experiments and practical engineering cases. The application results show that this method improves the shortcomings of MED and can extract fault features more effectively than the traditional MED method.


Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 611 ◽  
Author(s):  
Fuhe Yang ◽  
Xingquan Shen ◽  
Zhijian Wang

Under complicated conditions, the extraction of a multi-fault in gearboxes is difficult to achieve. Due to improper selection of methods, leakage diagnosis or misdiagnosis will usually occur. Ensemble Empirical Mode Decomposition (EEMD) often causes energy leakage due to improper selection of white noise during signal decomposition. Considering that only a single fault cycle can be extracted when MOMED (Multipoint Optimal Minimum Entropy Deconvolution) is used, it is necessary to perform the sub-band processing of the compound fault signal. This paper presents an adaptive gearbox multi-fault-feature extraction method based on Improved MOMED (IMOMED). Firstly, EEMD decomposes the signal adaptively and selects the intrinsic mode functions with strong correlation with the original signal to perform FFT (Fast Fourier transform); considering the mode-mixing phenomenon of EEMD, reconstruct the intrinsic mode functions with the same timescale, and obtain several intrinsic mode functions of the same scale to improve the entropy of fault features. There is a lot of white noise in the original signal, and EEMD can improve the signal-to-noise ratio of the original signal. Finally, through the setting of different noise-reduction intervals to extract fault features through MOMED. The proposed method is compared with EEMD and VMD (Variational Mode Decomposition) to verify its feasibility.


Author(s):  
Hongchao Wang ◽  
Jin Chen ◽  
Guangming Dong

The rolling bearing’s early stage fault feature is very weak for reasons of the signal attenuation phenomenon between the fault source and the sensor collecting the fault signal and the interference of environment noise such as the rotor rotating frequency and its harmonics and so on. The feature extraction of rolling bearing’s early weak fault is not only very important but also very hard. The minimum entropy de-convolution and Fast Kurtogram algorithm are combined in the paper for rolling bearing’s early stage weak fault feature extraction. The effect of transmission path is de-convolved effectively, and the impulses are clarified using minimum entropy de-convolution technique firstly. Then the obtained signal by minimum entropy de-convolution is handled by the Fast Kurtogram algorithm and an optimal filter is established. At last the envelope de-modulation is applied on the filtered signal and better feature extraction result is obtained compared with the other methods such as wavelet transform, frequency slice wavelet transformation and ensemble empirical mode decomposition. The effectiveness and advantages of the proposed method are verified through simulation signal and experiment.


2012 ◽  
Vol 190-191 ◽  
pp. 1371-1375
Author(s):  
Ping Hua Ju ◽  
Gen Bao Zhang

Early fault features of rotating machinery is very weak and is disturbed by strong noise generally. how to more accurately extract early (weak) fault features from signals is still a hot and difficult point of research of the discipline. An intensive study is given to basic features of rotating machinery early faults and common diagnosis method, And also summarized the research status of early diagnosis in the field of mechanical equipment signal feature extraction and fault diagnosis, analyzed the current problems, and finally briefly pointed out the development of early fault diagnosis in machinery applications.


Measurement ◽  
2020 ◽  
Vol 151 ◽  
pp. 107140 ◽  
Author(s):  
Jian Cheng ◽  
Yu Yang ◽  
Xin Li ◽  
Haiyang Pan ◽  
Junsheng Cheng

2014 ◽  
Vol 898 ◽  
pp. 961-964 ◽  
Author(s):  
Shang Kun Liu ◽  
Bin Pang ◽  
Gui Ji Tang

The rolling bearings fault feature under strong background noise is very weak for reasons of environment noise impact and signal attenuation. To extract the fault features of roller bearings effectively, a new method based on Minimum entropy deconvolution (MED) and slice bi-spectrum is proposed. The paper Firstly decreases the strong background noise of rolling bearing by the MED method, and then calculates the envelope signal of the de-noised signal. Finally, analyzes the envelope signal with slice bi-spectrum and extracted the fault characteristic frequency. The effectiveness of the proposed method was validated by analysis of both a simulated faulty bearing vibration signal and the experiment measured signal of rolling bearing, and it was also compared with the method of envelope spectrum.


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xinyu Wang ◽  
Jie Ma

In order to solve the problem that it is very difficult to extract fault features directly from the weak impact component of early fault signal of rolling bearing, a method combining continuous variational mode decomposition (SVMD) with modified MOMEDA based on Teager energy operator is proposed. Firstly, the low resonance impulse component in the fault signal is separated from the harmonic component and noise by SVMD, and then the Teager energy operator is used to enhance the impulse feature in the low resonance component to ensure that the accurate fault period is selected by the MOMOEDA algorithm. After further noise reduction by MOMEDA, the envelope spectrum of the signal is analyzed, and finally the fault location is determined. The results of simulation and experimental data show that this method can accurately and effectively extract the characteristic frequency of rolling bearing weak fault.


Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 667 ◽  
Author(s):  
Fen Yang ◽  
Ziming Kou ◽  
Juan Wu ◽  
Tengyu Li

In this paper, a novel weak fault features extraction scheme is proposed to extract weak fault features in head sheave bearings of floor-type multi-rope friction mine hoists in strong noise environments. A mutual information-based sample entropy (MI-SE) is proposed to select the effective intrinsic mode function (IMF). The numerical simulation presented in this paper has demonstrated that the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) has a poor performance on weak signals processing under a strong noise background, and fault features cannot be identified clearly. The de-noised signal is decomposed into several IMFs by the ICEEMDAN method, with the help of the minimum entropy deconvolution (MED), which works as a pre-filter to increase the kurtosis value by about 3.2 times. The envelope spectrum of the effective IMF selected by the MI-SE method shows almost all fault features clearly. An analogous experiment system was built to verify the feasibility of the proposed scheme, whose results have also shown that the proposed hybrid scheme has better performance compared with ICEEMDAN or MED on the weak fault features extraction under a strong noise background. This paper provides a novel method to diagnose the weak faults of the slow speed and heavy load rolling bearings in a strong noise environment.


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