scholarly journals Early bearing fault diagnosis based on the improved singular value decomposition method

2021 ◽  
Author(s):  
Lingli Cui ◽  
Mengxin Sun ◽  
Chunqing Zha
Keyword(s):  
Fault Diagnosis ◽  
Singular Value Decomposition ◽  
Decomposition Method ◽  
Singular Value ◽  
Bearing Fault ◽  
Singular Value Decomposition Method ◽  
Bearing Fault Diagnosis ◽  
Value Decomposition

Bearing Fault Diagnosis Using Singular Value Decomposition and Hidden Markov Modeling

2009 ◽  
Author(s):  
Dong Wang ◽  
Qiang Miao ◽  
Rui Sun ◽  
Hong-Zhong Huang
Keyword(s):  
Fault Diagnosis ◽  
Singular Value Decomposition ◽  
Hidden Markov ◽  
Singular Value ◽  
Practical Significance ◽  
Markov Modeling ◽  
Classification Rate ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Value Decomposition

Condition monitoring and fault diagnosis of bearings are of practical significance in industry. In order to get a feature containing different fault signatures, this paper uses Wavelet Transform (WT), Wavelet Lifting Scheme (WLS) and Empirical Mode Decomposition (EMD), respectively, to decompose signal into different frequency bands. Then, Singular Value Decomposition (SVD) is utilized to extract intrinsic characteristic of signal from obtained matrix. These singular value vectors are regarded as inputs to Hidden Markov Models (HMM) for identification of machinery health condition. In this research, the fault diagnosis system is validated by motor bearing data, including normal bearings, inner race fault bearings, outer race fault bearings and roller fault bearings. Analysis results show that this method is effective in bearing fault diagnosis and its classification rate is excellent.

scholarly journals Early Bearing Fault Diagnosis based on the Improved Singular Value Decomposition Method

2021 ◽  
Author(s):  
Lingli Cui ◽  
Mengxin Sun ◽  
Jinfeng Huang
Keyword(s):  
Singular Value Decomposition ◽  
Decomposition Method ◽  
Selection Index ◽  
Hankel Matrix ◽  
Singular Value ◽  
Bearing Fault ◽  
Singular Value Decomposition Method ◽  
Weak Fault ◽  
Value Decomposition ◽  
Fault Information

Abstract The traditional singular value decomposition (SVD) method is unable to diagnose the weak fault feature of bearings effectively, which means, it is difficult to retain the effective singular components (SCs). Therefore, a new singular value decomposition method, SVD based on the FIC (fault information content), is proposed, which takes the amplitude characteristics of fault feature frequency as the selection index FIC of singular components. Firstly, the Hankel matrix of the original signal is constructed and SVD is applied in the matrix. Secondly, the proposed index FIC is used to evaluate the information of the decomposed SCs. Finally, the SCs with fault information are selected and added to obtain the denoised signal. The results of bearing fault simulation signals and experimental signals show that compared with the traditional differential singular value decomposition (DS-SVD), the proposed method can select the singular components with larger amount of fault information, and is able to diagnose the fault under the heavy noise interference. The new method can be used for signal denoising and weak fault feature extraction.

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