Fault detection of a spherical tank using a genetic algorithm-based hybrid feature pool and k-nearest neighbor algorithm.

Hasan, Md. Junayed; Kim, Jong-Myon

Abstract

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.

Citation

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