eprintid: 13379
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/33/79
datestamp: 2023-11-10 03:27:56
lastmod: 2023-11-10 03:27:56
status_changed: 2023-11-10 01:51:01
type: article
metadata_visibility: show
creators_name: Arunthavanathan, R.
creators_name: Khan, F.
creators_name: Ahmed, S.
creators_name: Imtiaz, S.
creators_name: Rusli, R.
title: Fault detection and diagnosis in process system using artificial intelligence-based cognitive technique
ispublished: pub
keywords: Learning algorithms; Machine learning; Unsupervised learning, Cognitive model; Cognitive modelling; Fault conditions; Fault detection and classification; Fault detection and diagnosis; Laboratory experiments; Pilot scale system; Tennessee Eastman process, Fault detection
note: cited By 49
abstract: Fault detection and classifications using supervised learning algorithms are widely studied; however, lesser attention is given to fault detection using unsupervised learning. This work focused on the integration of unsupervised learning with cognitive modelling to detect and diagnose unknown fault conditions. It is achieved by integrating two techniques: (i) incremental one class algorithm to identify anomaly condition and introduce a new state of fault to the current fault states if an unknown fault occurs, and (ii) dynamic shallow neural network to learn and classify the fault state. The proposed framework is applied to the well-known Tennessee Eastman process and achieved significantly better results compared to results reported by earlier studies. Laboratory experiments are also performed using a pilot-scale system to test the validity of the approach. The results confirm the proposed framework as an effective way to detect and classify known and unknown faults in process operations. © 2019
date: 2020
publisher: Elsevier Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077647842&doi=10.1016%2fj.compchemeng.2019.106697&partnerID=40&md5=f386b588b5240f05fa621d7c0753e01a
id_number: 10.1016/j.compchemeng.2019.106697
full_text_status: none
publication: Computers and Chemical Engineering
volume: 134
refereed: TRUE
issn: 00981354
citation:   Arunthavanathan, R. and Khan, F. and Ahmed, S. and Imtiaz, S. and Rusli, R.  (2020) Fault detection and diagnosis in process system using artificial intelligence-based cognitive technique.  Computers and Chemical Engineering, 134.   ISSN 00981354