Canonical Variable Analysis for Fault Detection, System Identification and Performance Estimation

Journal article


Duan, F, Xiaochuan, L, Tariq, S, Ian, B and David, M (2017). Canonical Variable Analysis for Fault Detection, System Identification and Performance Estimation. Lecture Notes in Mechanical Engineering. 3, pp. 247-257.
AuthorsDuan, F, Xiaochuan, L, Tariq, S, Ian, B and David, M
Abstract

Condition monitoring of industrial processes can minimize downtime and maintenance costs while enhancing the safety of operation of plants and increasing the quality of products. Multivariate statistical methods are widely used for condition monitoring in industrial plants due to the rapid growth and advancement in data acquisition technology. However, the effectiveness of these methodologies in real industrial processes has not been fully investigated. This paper proposes a CVA-based approach for process fault identification, system modelling and performance estimation. The effectiveness of the proposed method was tested using data acquired from an operational industrial centrifugal compressor. The results indicate that CVA can be effectively used to identify abnormal operating conditions and predict performance degradation after the appearance of faults.

KeywordsCondition monitoring; Canonical variable analysis; Fault detection; System identification; Performance estimation
Year2017
JournalLecture Notes in Mechanical Engineering
Journal citation3, pp. 247-257
PublisherSpringer
ISSN2195-4356
Publication dates
Print25 Nov 2017
Publication process dates
Deposited19 Jul 2018
Accepted22 Nov 2017
Accepted author manuscript
License
CC BY 4.0
ISBN2195-4356
Book titleLecture Notes in Mechanical Engineering
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https://openresearch.lsbu.ac.uk/item/86wv1

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