eprintid: 9250
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/92/50
datestamp: 2023-11-09 16:21:13
lastmod: 2023-11-09 16:21:13
status_changed: 2023-11-09 16:14:38
type: article
metadata_visibility: show
creators_name: Hori, Y.
creators_name: Yamamoto, H.
creators_name: Suzuki, T.
creators_name: Okitsu, J.
creators_name: Nakamura, T.
creators_name: Maeda, T.
creators_name: Matsuo, T.
creators_name: Bt Zabiri, H.
creators_name: Tufa, L.D.
creators_name: Marappagounder, R.
title: Online anomaly detection of distillation tower system using adaptive resonance theory
ispublished: pub
note: cited By 6
abstract: An anomaly detection system based on adaptive response theory (ART) for industrial plants was proposed. The detection system has several ART applied to subsystems of the plant to narrow down the cause of the anomalies. An examination of online anomaly detection tests on whether or not the proposed system is applicable to a distillation tower system was performed. Four cases of anomaly operations, e.g., valve sticking and tray upset, that would cause quality or yield losses in the product were considered. Learning normal operation data revealed that the proposed system successfully detected the anomalies in all cases, and no false positives were noted in normal operation. Additionally, the system narrowed down the cause of the anomalies through using the results of each ART system, thus, demonstrating the applicability of the system for the distillation tower system.
date: 2017
publisher: Society of Chemical Engineers, Japan
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021771504&doi=10.1252%2fjcej.16we360&partnerID=40&md5=16b24700ec35c605987056f6ef0e5875
id_number: 10.1252/jcej.16we360
full_text_status: none
publication: Journal of Chemical Engineering of Japan
volume: 50
number: 6Speci
pagerange: 430-438
refereed: TRUE
issn: 00219592
citation:   Hori, Y. and Yamamoto, H. and Suzuki, T. and Okitsu, J. and Nakamura, T. and Maeda, T. and Matsuo, T. and Bt Zabiri, H. and Tufa, L.D. and Marappagounder, R.  (2017) Online anomaly detection of distillation tower system using adaptive resonance theory.  Journal of Chemical Engineering of Japan, 50 (6Speci).  pp. 430-438.  ISSN 00219592