Rolling Bearing Fault Diagnosis Based on Robust Principal Component Analysis Filtering and Envelope Spectrum Analysis

2016 ◽  
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Spectrum Analysis ◽  
Principal Component ◽  
Rolling Bearing ◽  
Component Analysis ◽  
Robust Principal Component Analysis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Envelope Spectrum

scholarly journals An Effective Bearing Fault Diagnosis Technique via Local Robust Principal Component Analysis and Multi-Scale Permutation Entropy

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 959 ◽  
Author(s):  
Mao Ge ◽  
Yong Lv ◽  
Yi Zhang ◽  
Cancan Yi ◽  
Yubo Ma
Keyword(s):  
Principal Component Analysis ◽  
Background Noise ◽  
Principal Component ◽  
Component Analysis ◽  
Permutation Entropy ◽  
Robust Principal Component Analysis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Diagnosis Technique

The acquired bearing fault signal usually reveals nonlinear and non-stationary nature. Moreover, in the actual environment, some other interference components and strong background noise are unavoidable, which lead to the fault feature signal being weak. Considering the above issues, an effective bearing fault diagnosis technique via local robust principal component analysis (LRPCA) and multi-scale permutation entropy (MSPE) was introduced in this paper. Robust principal component analysis (RPCA) has proven to be a powerful de-noising method, which can extract a low-dimensional submanifold structure representing signal feature from the signal trajectory matrix. However, RPCA can only handle single-component signal. Therefore, in order to suppress background noise, an improved RPCA method named LRPCA is proposed to decompose the signal into several single-components. Since MSPE can efficiently evaluate the dynamic complexity and randomness of the signals under different scales, the fault-related single-components can be identified according the MPSE characteristic of the signals. Thereafter, these identified components are combined into a one-dimensional signal to represent the fault feature component for further diagnosis. The numerical simulation experimentation and the analysis of bearing outer race fault data both verified the effectiveness of the proposed technique.

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