Dr Md Junayed Hasan j.hasan@rgu.ac.uk
Research Fellow A
Fault detection in metallic structures requires a detailed and discriminative feature pool creation mechanism to develop an effective condition monitoring system. Traditional fault detection methods incorporate handcrafted features either from the time, frequency or time-frequency domains. To explore the salient information provided by the acoustic emission (AE) signals, a hybrid of feature pool creation and an optimal features subset selection mechanism is proposed for crack detection in a spherical tank. The optimal hybrid feature pool creation process is composed of two major parts: (1) extraction of statistical features fromtime and frequency domains, aswell as extraction of traditional features associated with the AE signals; and (2) genetic algorithm (GA)-based optimal features subset selection. The optimal features subset is then provided to the k-nearest neighbor (k-NN) classifier to distinguish between normal (NC) and crack conditions (CC). Experimental results show that the proposed approach yields an average 99.8% accuracy for heath state classification. To validate the effectiveness of the proposed approach, it is compared to conventional non-linear dimensionality reduction techniques, as well as those without feature selection schemes. Experimental results show that the proposed approach outperforms conventional non-linear dimensionality reduction techniques, achieving at least 2.55% higher classification accuracy.
HASAN, M.J. and KIM, J.-M. 2019. Fault detection of a spherical tank using a genetic algorithm-based hybrid feature pool and k-nearest neighbor algorithm. Energies [online], 12(6): fault diagnosis and fault-tolerant control, article 991. Available from: https://doi.org/10.3390/en12060991
Journal Article Type | Article |
---|---|
Acceptance Date | Mar 11, 2019 |
Online Publication Date | Mar 14, 2019 |
Publication Date | Mar 31, 2019 |
Deposit Date | May 12, 2022 |
Publicly Available Date | May 30, 2022 |
Journal | Energies |
Electronic ISSN | 1996-1073 |
Publisher | MDPI |
Peer Reviewed | Peer Reviewed |
Volume | 12 |
Issue | 6 |
Article Number | 991 |
DOI | https://doi.org/10.3390/en12060991 |
Keywords | Acoustic emissions; Fault diagnosis; Genetic algorithm; Hybrid feature pool; k-NN classifier; Spherical tank; Statistical features |
Public URL | https://rgu-repository.worktribe.com/output/1664359 |
HASAN 2019 Fault detection (VOR)
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© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
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