eprintid: 11384
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/13/84
datestamp: 2023-11-10 03:25:54
lastmod: 2023-11-10 03:25:54
status_changed: 2023-11-10 01:15:08
type: conference_item
metadata_visibility: show
creators_name: Nawaz, M.
creators_name: Maulud, A.S.
creators_name: Zabiri, H.
title: Online process monitoring using multiscale principal component analysis
ispublished: pub
note: cited By 2; Conference of 4th Innovation and Analytics Conference and Exhibition, IACE 2019 ; Conference Date: 25 March 2019 Through 28 March 2019; Conference Code:150892
abstract: Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find faulty sensors timely and effectively. Process data from chemical processes are highly correlated and generally have multiscale features. Multiscale process monitoring techniques based on wavelets have been regarded as powerful tools because these can efficiently separate deterministic and stochastic features. An online multiscale fault detection approach using principal component analysis (PCA) is proposed in this paper by introducing a moving window into traditional wavelet transform. Various windows in wavelet decomposition are used to determine the appropriate window size for model development. The results demonstrate that the approximation and the highest detail functions are adequate to detect the fault. The proposed approach performance is validated using simulated data from a continuously stirred tank reactor (CSTR) system. The proposed method shows a substantial improvement over conventional PCA and multiscale PCA. © 2019 Author(s).
date: 2019
publisher: American Institute of Physics Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071638860&doi=10.1063%2f1.5121128&partnerID=40&md5=0c794244a14529cf8a5b7f6548a58470
id_number: 10.1063/1.5121128
full_text_status: none
publication: AIP Conference Proceedings
volume: 2138
refereed: TRUE
isbn: 9780735418813
issn: 0094243X
citation:   Nawaz, M. and Maulud, A.S. and Zabiri, H.  (2019) Online process monitoring using multiscale principal component analysis.  In: UNSPECIFIED.