scholarly journals A hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing

2018 ◽  
Vol 37 (4) ◽  
pp. 928-954 ◽  
Author(s):  
Jun Ma ◽  
Jiande Wu ◽  
Xiaodong Wang
Keyword(s):  
Fault Diagnosis ◽  
Decomposition Method ◽  
Energy Operator ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Singular Value ◽  
Local Mean Decomposition ◽  
Diagnosis Method ◽  
Local Mean ◽  
Teager Energy

Rolling bearing is one of the most crucial components in rotating machinery and due to their critical role, it is of great importance to monitor their operation conditions. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. Therefore, signal denoising preprocessing has become an essential part of condition monitoring and fault diagnosis. In the present study, a hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing is proposed. First, as a denoising preprocessing method, singular value difference spectrum denoising is applied to reduce the noise of the bearing vibration signal and improve the signal-to-noise ratio. Then, local mean decomposition method is used to decompose the denoised signals into several product functions. And product functions corresponding to the fault feature are selected according to the correlation coefficient criterion. Finally, Teager energy spectrum is analyzed by applying the Teager energy operator to the constructed amplitude modulation component. The proposed method is successfully applied to analyze the vibration signals collected from an experimental motive rolling bearing and rolling bearing of the self-made rotor experimental platform. The experimental results demonstrate that the proposed singular value difference spectrum denoising and local mean decomposition method can achieve fairly or slightly better performance than the normal local mean decomposition-Teager energy operator method, fast kurtogram, and the wavelet denoising and local mean decomposition method.

scholarly journals Research on Rolling Bearing Fault Feature Extraction Based on Singular Value Decomposition considering the Singular Component Accumulative Effect and Teager Energy Operator

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Longlong Li ◽  
Yahui Cui ◽  
Runlin Chen ◽  
Lingping Chen ◽  
Lihua Wang
Keyword(s):  
Fault Diagnosis ◽  
Singular Value Decomposition ◽  
Energy Operator ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Singular Value ◽  
Teager Energy Operator ◽  
Singular Component ◽  
Value Decomposition ◽  
Teager Energy

The extraction of impulsive signatures from a vibration signal is vital for fault diagnosis of rolling element bearings, which are always whelmed by noise, especially in the early stage of defect development. Aiming at the weak defect diagnosis, kurtosis of Teager energy operator (KTEO) spectrum is employed to indicate the fault information capacity of a spectrum, and considering the accumulative effect of a singular component, accumulative kurtosis of TEO (AKTEO) is firstly proposed to determine the proper signal reconstructed order during vibration signal processing using singular value decomposition (SVD). Then, a vibration processing scheme named SVD-AKTEO is designed where an iteration is employed to reflect an accumulative singular effect by kurtosis of TEO spectrum. Finally, the fault diagnosis results can be extracted from the TEO spectrum output by SVD-AKTEO. Simulation data and real data from a run-to-failure experiment of a rolling bearing are adopted to validate the efficiency, and comparative analysis demonstrates the feasibility to detect the early defect of the rolling bearing.

Feature extraction of rolling bearing fault signal based on local mean decomposition and Teager energy operator

2017 ◽  
Vol 69 (6) ◽  
pp. 872-880 ◽  
Author(s):  
Jianhua Cai
Keyword(s):  
Fourier Method ◽  
Energy Operator ◽  
Rolling Bearing ◽  
Transform Method ◽  
Content Type ◽  
Local Mean Decomposition ◽  
Teager Energy Operator ◽  
Marginal Spectrum ◽  
Local Mean ◽  
Teager Energy

Purpose This paper aims to explore a new way to extract the fault feature of a rolling bearing signal on the basis of a combinatorial method. Design/methodology/approach By combining local mean decomposition (LMD) with Teager energy operator, a new feature-extraction method of a rolling bearing fault signal was proposed, called the LMD–Teager transform method. The principles and steps of method are presented, and the physical meaning of the time–frequency power spectrum and marginal spectrum is discussed. On the basis of comparison with the fast Fourier transform method, a simulated non-stationary signal was processed to verify the effect of the new method. Meanwhile, an analysis was conducted by using the recorded vibration signals which include inner race, out race and bearing ball fault signal. Findings The results show that the proposed method is more suitable for the non-stationary fault signal because the LMD–Teager transform method breaks through the difficulty of the Fourier transform method that can process only the stationary signal. The new method can extract more useful information and can provide better analysis accuracy and resolution compared with the traditional Fourier method. Originality/value Combining the advantage of the local mean decomposition and the Teager energy operator, the LMD–Teager method suits the nature of the fault signal. A marginal spectrum obtained from the LMD–Teager method minimizes the estimation bias brought about by the non-stationarity of the fault signal. So, the LMD–Teager transform has better analysis accuracy and resolution than the traditional Fourier method, which provides a good alternative for fault diagnosis of the rolling bearing.

A Study on Fault Diagnosis of Gears Based on Local Mean Decomposition Method

2013 ◽  
Vol 683 ◽  
pp. 899-902
Author(s):  
Qiang Pan ◽  
Deng Hong Xiao ◽  
Tian He
Keyword(s):  
Fourier Transform ◽  
Fault Diagnosis ◽  
Hilbert Transform ◽  
Decomposition Method ◽  
Local Mean Decomposition ◽  
Local Mean ◽  
Component Amplitude

In present paper, the effectiveness of local mean decomposition (LMD) method to signals of fault gears, which are multi-component amplitude modulated and frequency modulated, is demonstrated. A series of tests on wearing and broken tooth of gears are conducted. And the fault characteristics extracted by Fourier transform, Hilbert transform and LMD are compared. The results validate that LMD method is an effective way to extract the characteristics of fault gears and improve the accuracy of fault diagnosis of gears since it is able to reduce effect of false components.

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