Rolling element bearing diagnosis based on singular value decomposition and composite squared envelope spectrum

2021 ◽  
Vol 148 ◽  
pp. 107174
Author(s):  
Lang Xu ◽  
Steven Chatterton ◽  
Paolo Pennacchi
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Rolling Element Bearing ◽  
Rolling Element ◽  
Element Bearing ◽  
Value Decomposition ◽  
Envelope Spectrum

Rolling element bearing fault diagnostics: Development of health index

2016 ◽  
Vol 231 (21) ◽  
pp. 3923-3939 ◽  
Author(s):  
HS Kumar ◽  
Srinivasa P Pai ◽  
NS Sriram ◽  
GS Vijay
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Rolling Element Bearing ◽  
Variable Load ◽  
Fisher Discriminant Analysis ◽  
Average Value ◽  
Health Indices ◽  
Rolling Element ◽  
Element Bearing ◽  
Value Decomposition

This article develops and compares health indices using different approaches namely singular value decomposition, average value of the cumulative feature and Mahalanobis distance for assessing the rolling element bearing condition. The vibration signals for four conditions of rolling element bearing are acquired from a customized bearing test rig under variable load conditions. Seventeen statistical features are extracted from wavelet coefficients of the denoised signals. Feature selection is performed using singular value decomposition and kernel Fisher discriminant analysis. These selected features are used in these three approaches to develop health indices. Finally, a comparison of the three proposed approaches is made to select the best approach which can be effectively used for fault diagnosis of rolling element bearings.

Research on Rolling Element Bearing Fault Diagnosis Based on Singular Value Decomposition and Kurtosis Criterion

2013 ◽  
Vol 432 ◽  
pp. 304-309 ◽  
Author(s):  
Xiao Lin Wang ◽  
Yong Xiang Zhang ◽  
Jie Ping Zhu ◽  
Zhong Qi Shi
Keyword(s):  
Singular Value Decomposition ◽  
Vibration Signal ◽  
Singular Value ◽  
Rolling Element Bearing ◽  
Feature Extraction Method ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
New Feature ◽  
Value Decomposition ◽  
Fault Information

In order to extract the faint fault information from complicated vibration signal of bearing, a new feature extraction method based on singular value decomposition (SVD) and kurtosis criterion is proposed in my work. According to the method, a group of component signals are obtained firstly using SVD, then component signals with equal kurtosis are selected to be summed together, and the weak fault signal is clearly extracted. The effectiveness of the method is demonstrated on both simulated signal and actual data.

Spalling Size Evaluation of Rolling Element Bearing Using Acoustic Emission

2013 ◽  
Vol 569-570 ◽  
pp. 497-504 ◽  
Author(s):  
An Bo Ming ◽  
Zhao Ye Qin ◽  
Wei Zhang ◽  
Fu Lei Chu
Keyword(s):  
Acoustic Emission ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Outer Race ◽  
Cepstrum Analysis ◽  
Rolling Element ◽  
Rolling Elements ◽  
Element Bearing ◽  
Envelope Spectrum ◽  
Size Evaluation

Spalling of the races or rolling elements is one of the most common faults in rolling element bearings. Exact estimation of the spall size is helpful to the life prediction for rolling element bearings. In this paper, the dual-impulsive phenomenon in the response of a spalled rolling element bearing is investigated experimentally, where the acoustic emission signals are utilized. A new method is proposed to estimate the spall size by extracting the envelope of harmonics of the ball passing frequency on the outer race from the squared envelope spectrum. Compared with the cepstrum analysis, the proposed procedure shows more powerful anti-noise ability in the fault size evaluation.

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