eprintid: 16968
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/69/68
datestamp: 2023-12-19 03:23:26
lastmod: 2023-12-19 03:23:26
status_changed: 2023-12-19 03:07:13
type: article
metadata_visibility: show
creators_name: Ali, H.
creators_name: Maulud, A.S.
creators_name: Zabiri, H.
creators_name: Nawaz, M.
creators_name: Suleman, H.
creators_name: Taqvi, S.A.A.
title: Multiscale Principal Component Analysis-Signed Directed Graph Based Process Monitoring and Fault Diagnosis
ispublished: pub
note: cited By 10
abstract: The chemical process industry has become the backbone of the global economy. The complexities of chemical process systems have been increased in the last two decades due to online sensor technology, plant-wide automation, and computerized measurement devices. Principal component analysis (PCA) and signed directed graph (SDG) are some of the quantitative and qualitative monitoring techniques that have been widely applied for chemical fault detection and diagnosis (FDD). The conventional PCA-SDG algorithm is a single-scale FDD representation origin, which cannot effectively solve multiple FDD representation origins. The multiscale PCA-SDG wavelet-based monitoring technique has potential because it easily distinguishes between deterministic and stochastic characteristics. This study uses multiscale PCA-SDG to detect, diagnose the root cause and identify the fault propagation path. The proposed method is applied to a continuous stirred tank reactor system to validate its effectiveness. The propagation route of most process failures is detected, identified, and diagnosed, which is well-aligned with the fault description, demonstrating a satisfactory performance of the suggested technique for monitoring the process failures. © 2022 The Authors. Published by American Chemical Society.
date: 2022
publisher: American Chemical Society
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126618204&doi=10.1021%2facsomega.1c06839&partnerID=40&md5=6ab8fdb9682fc482cc58da920444c016
id_number: 10.1021/acsomega.1c06839
full_text_status: none
publication: ACS Omega
volume: 7
number: 11
pagerange: 9496-9512
refereed: TRUE
issn: 24701343
citation:   Ali, H. and Maulud, A.S. and Zabiri, H. and Nawaz, M. and Suleman, H. and Taqvi, S.A.A.  (2022) Multiscale Principal Component Analysis-Signed Directed Graph Based Process Monitoring and Fault Diagnosis.  ACS Omega, 7 (11).  pp. 9496-9512.  ISSN 24701343