Short-time matrix series based singular value decomposition for rolling bearing fault diagnosis

2013 ◽  
Vol 34 (1-2) ◽  
pp. 218-230 ◽  
Author(s):  
Feiyun Cong ◽  
Jin Chen ◽  
Guangming Dong ◽  
Fagang Zhao
Keyword(s):  
Fault Diagnosis ◽  
Singular Value Decomposition ◽  
Rolling Bearing ◽  
Singular Value ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Value Decomposition ◽  
Short Time ◽  
Matrix Series

A sequential feature extraction method based on discrete wavelet transform, phase space reconstruction, and singular value decomposition and an improved extreme learning machine for rolling bearing fault diagnosis

2017 ◽  
Vol 232 (6) ◽  
pp. 635-649 ◽  
Author(s):  
DZ Li ◽  
X Zheng ◽  
QW Xie ◽  
QB Jin
Keyword(s):  
Fault Diagnosis ◽  
Phase Space ◽  
Extreme Learning Machine ◽  
Rolling Bearing ◽  
Singular Value ◽  
Discrete Wavelet ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Learning Machine ◽  
Value Decomposition

A novel fault diagnosis approach based on a combination of discrete wavelet transform, phase space reconstruction, singular value decomposition, and improved extreme learning machine is presented in rolling bearing fault identification and classification. The proposed method provides proper solutions for improving the accuracy of faults classification. To achieve this goal, initial signals are divided into sub-band wavelet coefficients using discrete wavelet transform. Then, each of sub-band is mapped into three-dimensional space using the phase space reconstruction method to completely describe characteristics in the high dimension. Thereafter, singular values are calculated by singular value decomposition method, which demonstrate crucial variances in original vibration signal. Lastly, an improved extreme learning machine is adopted as a classifier for fault classification. The proposed method is applied to the rolling bearing fault diagnosis with non-linear and non-stationary characteristics. Based on outputs of the improved extreme learning machine, the working condition and fault location could be determined accurately and quickly. Achieved results, compared with other schemes, show that the proposed scheme in this article can be regarded as an effective and reliable method for rolling bearing fault diagnosis.

scholarly journals Rolling Bearing Fault Diagnosis under Variable Conditions Using Hilbert-Huang Transform and Singular Value Decomposition

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hongmei Liu ◽  
Xuan Wang ◽  
Chen Lu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Singular Value ◽  
Instantaneous Amplitude ◽  
Elman Neural Network ◽  
Hilbert Huang Transform ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Value Decomposition

Fault diagnosis precision for rolling bearings under variable conditions has always been unsatisfactory. To solve this problem, a fault diagnosis method combining Hilbert-Huang transform (HHT), singular value decomposition (SVD), and Elman neural network is proposed in this paper. The method includes three steps. First, instantaneous amplitude matrices were obtained by using HHT from rolling bearing signals. Second, the singular value vector was acquired by applying SVD to the instantaneous amplitude matrices, thus reducing the dimension of the instantaneous amplitude matrix and obtaining the fault feature insensitive to working condition variation. Finally, an Elman neural network was applied to the rolling bearing fault diagnosis under variable working conditions according to the extracted feature vector. The experimental results show that the proposed method can effectively classify rolling bearing fault modes with high precision under different operating conditions. Moreover, the performance of the proposed HHT-SVD-Elman method has an advantage over that of EMD-SVD or WPT-PCA for feature extraction and Support Vector Machine (SVM) or Extreme Learning Machine (ELM) for classification.

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.

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