Bearing fault diagnosis based on Alpha-stable distribution feature extraction and wSVM classifier

Chouri, Brahim; Aroussi, Mohamed El; Tabaa, Mohamed; Jarrou, Abderrahmane; Fabrice, M.; Dandache, Abbas

doi:10.1109/icm.2015.7438042

Cited by 1 publication

(1 citation statement)

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“…Fei et al [9] raised a characteristics selection technology to determine the fault-sensitive features by combining ARI and sum of within-class MD. Considering that the stable distribution can extract features with high discriminating ability, Chouri et al [24] combined alpha-stable distribution feature extraction with the weighted support vector machine (WSVM) to extract features efficiently. For the problem of nonsensitive characteristics in the features set, Liu et al [25] raised a characteristic selection approach based on sensitive characteristic extraction and nonlinear feature fusion, which used CDET to choose sensitive characteristics and weighing them and then used locality preserving projections (LPP) to reduce the size of the weighted sensitive features for getting more sensitive characteristics.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

et al. 2021

Shock and Vibration

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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.

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Section: Introductionmentioning

confidence: 99%

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

Chen

et al. 2021

Shock and Vibration

View full text Add to dashboard Cite

show abstract

Bearing fault diagnosis based on Alpha-stable distribution feature extraction and wSVM classifier

Cited by 1 publication

References 25 publications

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

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

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