A Rolling Element Bearing Diagnosis Method Based on Singular Value Decomposition and Squared Envelope Spectrum

2021 ◽  
Author(s):  
Lang Xu ◽  
Steven Chatterton ◽  
Paolo Pennacchi
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Rolling Element Bearing ◽  
Rolling Element ◽  
Diagnosis Method ◽  
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.

A Novel Fault Diagnosis Method Based on Time-Frequency Image Recognition

2014 ◽  
Vol 687-691 ◽  
pp. 3569-3573 ◽  
Author(s):  
Wei Gang Wang ◽  
Zhan Sheng Liu
Keyword(s):  
Fault Diagnosis ◽  
Image Recognition ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Time Frequency ◽  
Rolling Element ◽  
Diagnosis Method ◽  
Element Bearing ◽  
Vibration Time ◽  
Pyramid Matching

A novel intelligent fault diagnosis method based on vibration time-frequency image recognition is proposed in this paper. First, Smooth pseudo Wigner-Ville distribution (SPWVD) is employed to represent the time-frequency distribution characteristics. Then, the features of time-frequency images are extracted by using locality-constrained linear coding (LLC) and spatial pyramid matching. Next, we use the support vector machine to identify these feature vectors for realizing intelligent fault detection. The promise of our algorithm is illustrated by performing above procedures on the vibration signals measured from rolling element bearing with sixteen operating states. Experimental results show that the proposed method can acquire higher diagnosis accuracy compared with the ScSPM method in rolling element bearing diagnosis.

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