Nawaz, M. and Maulud, A.S. and Zabiri, H. (2021) Multiscale fault classification framework using kernel principal component analysis and k-nearest neighbors for chemical process system. In: UNSPECIFIED.
Full text not available from this repository.Abstract
Process monitoring techniques in chemical process systems help to improve product quality and plant safety. Multiscale classification plays a crucial role in the monitoring of chemical processes. However, there is a problem in coping with high-dimensional correlated data produced by complex, nonlinear processes. Therefore, an improved multiscale fault classification framework has been proposed to enhance the fault classification ability in nonlinear chemical process systems. This framework combines wavelet transform (WT), kernel principal component analysis (KPCA), and K nearest neighbors (KNN) classifier. Initially, a moving window-based WT is used to extract multiscale information from process data in time and frequency simultaneously at different scales. The resulting wavelet coefficients are reconstructed and fed into the KPCA to produce feature vectors. In the final step, these vectors have been used as inputs for the KNN classifier. The performance of the proposed multi-scale KPCA-KNN framework is analyzed and compared using a continuous stirred tank reactor (CSTR) system as a case study. The results show that the proposed multiscale KPCA-KNN framework has a high success rate over PCA-KNN and KPCA-KNN methods. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/)
Item Type: | Conference or Workshop Item (UNSPECIFIED) |
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Additional Information: | cited By 1; Conference of 2021 International Conference on Process Engineering and Advanced Materials, ICPEAM2020 ; Conference Date: 13 July 2021 Through 15 July 2021; Conference Code:185461 |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 10 Nov 2023 03:29 |
Last Modified: | 10 Nov 2023 03:29 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/14750 |