scholarly journals Rolling Bearing Fault Diagnosis Using Modified LFDA and EMD With Sensitive Feature Selection

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 3715-3730 ◽  
Author(s):  
Xiao Yu ◽  
Fei Dong ◽  
Enjie Ding ◽  
Shoupeng Wu ◽  
Chunyang Fan
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

scholarly journals Rolling Bearing Fault Diagnosis Based on Domain Adaptation and Preferred Feature Selection under Variable Working Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-27
Author(s):  
Xiao Yu ◽  
Wei Chen ◽  
Chuanlong Wu ◽  
Enjie Ding ◽  
Yuanyuan Tian ◽  
...  
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Domain Adaptation ◽  
Rolling Bearing ◽  
Machine Learning Techniques ◽  
Bearing Fault ◽  
Method Transfer ◽  
Bearing Fault Diagnosis ◽  
Manifold Structure

In real industrial scenarios, with the use of conventional machine learning techniques, data-driven diagnosis models have a limitation that it is difficult to achieve the desirable fault diagnosis performance, and the reason is that the training and testing datasets are assumed to have the same feature distributions. To address this problem, a novel bearing fault diagnosis framework based on domain adaptation and preferred feature selection is proposed, in that the model trained by the labeled data collected from a working condition can be applied to diagnose a new but similar target data collected from other working conditions. In this framework, an improved domain adaptation method, transfer component analysis with preserving local manifold structure (TCAPLMS), is proposed to reduce the differences in the data distributions between different domain datasets and, at the same time, take the label information of feature dataset and the local manifold structure of feature data into consideration. Furthermore, preferred feature selection by fault sensitivity and feature correlation (PSFFC) is embedded into this framework for selecting features which are more beneficial to fault pattern recognition and reduce the redundancy of feature set. Finally, vibration datasets collected from two test platforms are used for experimental analysis. The experimental results validate that the proposed method can obviously improve diagnosis accuracy and has significant potential benefits towards actual industrial scenarios.

scholarly journals Rolling bearing fault diagnosis method based on VMD and LSSVM

2021 ◽  
Vol 1792 (1) ◽  
pp. 012035
Author(s):  
Xingtong Zhu ◽  
Zhiling Huang ◽  
Jinfeng Chen ◽  
Junhao Lu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method
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