scholarly journals Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5541
Author(s):  
Kai Zheng ◽  
Yin Bai ◽  
Jingfeng Xiong ◽  
Feng Tan ◽  
Dewei Yang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
State Of The Art ◽  
Hankel Matrix ◽  
Rolling Bearing ◽  
The State ◽  
Low Rank ◽  
Sparse Decomposition ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Value Decomposition

Singular value decomposition (SVD) methods have aroused wide concern to extract the periodic impulses for bearing fault diagnosis. The state-of-the-art SVD methods mainly focus on the low rank property of the Hankel matrix for the fault feature, which cannot achieve satisfied performance when the background noise is strong. Different to the existing low rank-based approaches, we proposed a simultaneously low rank and group sparse decomposition (SLRGSD) method for bearing fault diagnosis. The major contribution is that the simultaneously low rank and group sparse (SLRGS) property of the Hankel matrix for fault feature is first revealed to improve performance of the proposed method. Firstly, we exploit the SLRGS property of the Hankel matrix for the fault feature. On this basis, a regularization model is formulated to construct the new diagnostic framework. Furthermore, the incremental proximal algorithm is adopted to achieve a stationary solution. Finally, the effectiveness of the SLRGSD method for enhancing the fault feature are profoundly validated by the numerical analysis, the artificial bearing fault experiment and the wind turbine bearing fault experiment. Simulation and experimental results indicate that the SLRGSD method can obtain superior results of extracting the incipient fault feature in both performance and visual quality as compared with the state-of-the-art methods.

Feature extraction for rolling bearing fault diagnosis by electrostatic monitoring sensors

2014 ◽  
Vol 229 (10) ◽  
pp. 1887-1903 ◽  
Author(s):  
Ying Zhang ◽  
Hongfu Zuo ◽  
Fang Bai
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Hankel Matrix ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Quantitative Indicator ◽  
Bearing Fault Diagnosis

There are mainly two problems with the current feature extraction methods used in the electrostatic monitoring of rolling bearings, which affect their abilities to identify early faults: (1) since noises are mixed in the electrostatic signals, it is difficult to extract weak early fault features; (2) traditional time and frequency domain features have limited ability to provide a quantitative indicator of degradation state. With regard to these two problems, a new feature extraction method for rolling bearing fault diagnosis by electrostatic monitoring sensors is proposed in this paper. First, the spectrum interpolation is adopted to suppress the power-frequency interference in the electrostatic signal. Then the resultant signal is used to construct Hankel matrix, the number of useful components is automatically selected based on the difference spectrum of singular values, after that the signal is reconstructed to remove background noises and random pulses. Finally, the permutation entropy of the denoised signal is calculated and smoothed using the exponential weighted moving average method, which is used to be a quantitative indicator of bearing performance state. The simulation and experimental results show that the proposed method can effectively remove noises and significantly bring forward the time when early faults are detected.

High Resonance Component of Resonance-Based Sparse Decomposition Application in Extraction of Rolling Bearing Fault Information

2013 ◽  
Vol 753-755 ◽  
pp. 2290-2296 ◽  
Author(s):  
Wen Tao Huang ◽  
Yin Feng Liu ◽  
Pei Lu Niu ◽  
Wei Jie Wang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Frequency Resolution ◽  
Sparse Decomposition ◽  
High Resonance ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Information Frequency ◽  
Fault Information

In the early fault diagnosis of rolling bearing, the vibration signal is mixed with a lot of noise, resulting in the difficulties in analysis of early weak fault signal. This article introduces resonance-based signal sparse decomposition (RSSD) into rolling bearing fault diagnosis, and studies the fault information contained in high resonance component and low resonance component. This article compares the effect of the two resonance components to extract rolling bearing fault information in four aspects: the amount of fault information, frequency resolution of subbands, sensitivity to noise and immunity to autocorrelation processing. We find that the high resonance component has greater advantage in extraction of rolling bearing fault information, and it is able to indicate rolling bearing failure accurately.

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.

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