Resilient fault detection for industrial process using adaptive Fisher discriminant analysis with wavelet denoising

Resilient fault detection for industrial process using adaptive Fisher discriminant analysis with wavelet denoising

In this paper, fault detection in industrial process using adaptive fisher discriminant analysis (AFDA) with optimization of wavelet denoising (WD) parameters is discussed. Particle swarm optimization (PSO) is used to set the wavelet denoising parameters. Among several important factors in effective...

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Bibliographic Details
Main Authors: Faizan E. Mustafa, Ali M. El-Rifaie, M.M.R. Ahmed, Fahmi Elsayed, Hasnain Ahmad, Ijaz Ahmed
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Results in Engineering
Subjects:
Adaptive FDA
False alarm rate
Industrial process
Particle swarm optimization
Missed detection rate
Wavelet denoising
Online Access:http://www.sciencedirect.com/science/article/pii/S2590123025027379
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Summary:In this paper, fault detection in industrial process using adaptive fisher discriminant analysis (AFDA) with optimization of wavelet denoising (WD) parameters is discussed. Particle swarm optimization (PSO) is used to set the wavelet denoising parameters. Among several important factors in effective fault detection, false alarm rate (FAR) and missed detection rate (MDR) are prominent. The efficacy of the fault detection system has been demonstrated by applying it to a rotating machinery test rig developed at the Pakistan Institute of Engineering and Applied Sciences (PIEAS). Results have shown that the proposed technique can reduce the false alarm rate and missed detection rate to 1% as compared to the adaptive fisher discriminant analysis and fisher discriminant analysis.
ISSN:2590-1230

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