eprintid: 19626
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/96/26
datestamp: 2024-06-04 14:19:22
lastmod: 2024-06-04 14:19:22
status_changed: 2024-06-04 14:15:27
type: article
metadata_visibility: show
creators_name: Ali, H.
creators_name: Zhang, Z.
creators_name: Safdar, R.
creators_name: Rasool, M.H.
creators_name: Yao, Y.
creators_name: Yao, L.
creators_name: Gao, F.
title: Fault detection using machine learning based dynamic ICA-distributed CCA: Application to industrial chemical process
ispublished: pub
note: cited By 0
abstract: Unexpected accidents and events in industrial chemical processes have resulted in a considerable number of causalities and property damage. Safety process management in industrial chemical processes is critical to avoid and ensure casualties and property damage. However, due to the immense scope and high complexity of current industrial chemical processes, the traditional safety process management approaches cannot address these challenges to attain adequate fault detection accuracy. To address this issue, an innovative machine learning-based distributed canonical correlation analysis-dynamic independent component analysis (DICA-DCCA) approach is needed to improve the fault detection effectiveness of complicated systems. The (DICA-DCCA) model could potentially detect anomalies and faults in industrial chemical data by utilizing three essential statistics:Id2,Ie2and squared prediction error (SPE). The practical effectiveness of the proposed frameworks is evaluated and compared using a continuous stirred tank reactor (CSTR) framework as a standard benchmark study. The research findings present that the suggested (DICA-DCCA) approach is more resilient and effective in detecting abnormalities and faults than the ICA and DICA approaches with FDR 100  and FAR 0 . The implied research approach is robust, operational, and productive. © 2024 The Author(s)
date: 2024
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192766297&doi=10.1016%2fj.dche.2024.100156&partnerID=40&md5=2c95ef15f9624f8242896a32d8240a71
id_number: 10.1016/j.dche.2024.100156
full_text_status: none
publication: Digital Chemical Engineering
volume: 11
refereed: TRUE
citation:   Ali, H. and Zhang, Z. and Safdar, R. and Rasool, M.H. and Yao, Y. and Yao, L. and Gao, F.  (2024) Fault detection using machine learning based dynamic ICA-distributed CCA: Application to industrial chemical process.  Digital Chemical Engineering, 11.