Weak Fault Diagnosis Method for Subway Bearing Based on Multi-scale Class Distance

2021 ◽  
pp. 281-294
Author(s):  
Yin Tian
Keyword(s):  
Fault Diagnosis ◽  
Multi Scale ◽  
Diagnosis Method ◽  
Weak Fault

scholarly journals A new weak fault diagnosis method based on multi-scale wavelet noise tuning cascaded multi-stable stochastic resonance

2018 ◽  
Vol 20 (1) ◽  
pp. 258-271 ◽  
Author(s):  
Dongying Han ◽  
Shujun An ◽  
Peiming Shi ◽  
Ying Zhang
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Multi Scale ◽  
Diagnosis Method ◽  
Weak Fault

A novel weak fault diagnosis method for rolling bearings based on LSTM considering quasi-periodicity

2021 ◽  
pp. 107413
Author(s):  
Dawei Gao ◽  
Yongsheng Zhu ◽  
Zhijun Ren ◽  
Ke Yan ◽  
Wei Kang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearings ◽  
Diagnosis Method ◽  
Weak Fault

Motor Bearing Fault Diagnosis Based on MSICA-LSSVM

2011 ◽  
Vol 55-57 ◽  
pp. 747-752
Author(s):  
Zhong Hai Li ◽  
Hao Fei Mao ◽  
Jian Guo Cui ◽  
Yan Zhang
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Decomposition ◽  
Principal Component ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Fault Type ◽  
Diagnosis Method ◽  
Motor Bearing

The paper presents a motor bearing fault diagnosis method based on MSICA (Multi-scale Independent Principal Component Analysis) and LSSVM (Least Squares Support Vector Machine). MSICA is introduced into motor fault diagnosis. First, wavelet decomposition is used, and then ICA models are built by wavelet coefficients in each scale, which detect fault, and finally LSSVM is used to classify fault type. Conclusions are obtained from the analysis of the experimental data provided by Case Western Reserve University’s Bearing Data Website. And it indicates that the proposed method is simple and effective.

scholarly journals An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhiwu Shang ◽  
Wanxiang Li ◽  
Maosheng Gao ◽  
Xia Liu ◽  
Yan Yu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Information Entropy ◽  
Feature Fusion ◽  
Fault Classification ◽  
Intelligent Fault Diagnosis ◽  
Test Bed ◽  
Multi Scale ◽  
Deep Feature ◽  
Diagnosis Method

AbstractFor a single-structure deep learning fault diagnosis model, its disadvantages are an insufficient feature extraction and weak fault classification capability. This paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information entropy. First, a normal autoencoder, denoising autoencoder, sparse autoencoder, and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure. A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep features. Finally, the advantage of the deep belief network probability model is used as the fault classifier to identify the faults. The effectiveness of the proposed method was verified by a gearbox test-bed. Experimental results show that, compared with traditional and existing intelligent fault diagnosis methods, the proposed method can obtain representative information and features from the raw data with higher classification accuracy.

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