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Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors
Joint Authors
Islam, Md. Rashedul
Khan, Sheraz Ali
Kim, Jong-Myon
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-12-21
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
Optimal feature distribution and feature selection are of paramount importance for reliable fault diagnosis in induction motors.
This paper proposes a hybrid feature selection model with a novel discriminant feature distribution analysis-based feature evaluation method.
The hybrid feature selection employs a genetic algorithm- (GA-) based filter analysis to select optimal features and a k-NN average classification accuracy-based wrapper analysis approach that selects the most optimal features.
The proposed feature selection model is applied through an offline process, where a high-dimensional hybrid feature vector is extracted from acquired acoustic emission (AE) signals, which represents a discriminative fault signature.
The feature selection determines the optimal features for different types and sizes of single and combined bearing faults under different speed conditions.
The effectiveness of the proposed feature selection scheme is verified through an online process that diagnoses faults in an unknown AE fault signal by extracting only the selected features and using the k-NN classification algorithm to classify the fault condition manifested in the unknown signal.
The classification performance of the proposed approach is compared with those of existing state-of-the-art average distance-based approaches.
Our experimental results indicate that the proposed approach outperforms the existing methods with regard to classification accuracy.
American Psychological Association (APA)
Islam, Md. Rashedul& Khan, Sheraz Ali& Kim, Jong-Myon. 2015. Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors. Journal of Sensors،Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1110585
Modern Language Association (MLA)
Islam, Md. Rashedul…[et al.]. Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors. Journal of Sensors No. 2016 (2016), pp.1-16.
https://search.emarefa.net/detail/BIM-1110585
American Medical Association (AMA)
Islam, Md. Rashedul& Khan, Sheraz Ali& Kim, Jong-Myon. Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors. Journal of Sensors. 2015. Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1110585
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1110585