Health state classification of a spherical tank using a hybrid bag of features and K-nearest neighbor.

Hasan, Md. Junayed; Kim, Jaeyoung; Kim, Cheol Hong; Kim, Jong-Myon

Abstract

Feature analysis puts a great impact in determining the various health conditions of mechanical vessels. To achieve balance between traditional feature extraction and the automated feature selection process, a hybrid bag of features (HBoF) is designed for multiclass health state classification of spherical tanks in this paper. The proposed HBoF is composed of (a) the acoustic emission (AE) features and (b) the time and frequency based statistical features. A wrapper-based feature chooser algorithm, Boruta, is utilized to extract the most intrinsic feature set from HBoF. The selective feature matrix is passed to the multi-class k-nearest neighbor (k-NN) algorithm to differentiate among normal condition (NC) and two faulty conditions (FC1 and FC2). Experimental results demonstrate that the proposed methodology generates an average 99.7% accuracy for all working conditions. Moreover, it outperforms the existing state-of-art works by achieving at least 19.4%.

Citation

HASAN, M.J., KIM, J., KIM, C.H. and KIM, J.-M. 2020. Health state classification of a spherical tank using a hybrid bag of features and K-nearest neighbor. Applied sciences [online], 10(7), article 2525. Available from: https://doi.org/10.3390/app10072525

• Journal Article Type: Article
• Acceptance Date: Apr 2, 2020
• Online Publication Date: Apr 6, 2020
• Publication Date: Apr 1, 2020
• Deposit Date: May 13, 2022
• Publicly Available Date: May 30, 2022
• Journal: Applied Sciences (Switzerland)
• Electronic ISSN: 2076-3417
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 7
• Article Number: 2525
• DOI: https://doi.org/10.3390/app10072525
• Keywords: Spherical tank; AE features; Boruta; Fault diagnosis; Multiclass classification
• Public URL: https://rgu-repository.worktribe.com/output/1664512