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2018 | OriginalPaper | Buchkapitel

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

verfasst von : Xiaochuan Li, Fang Duan, Tariq Sattar, Ian Bennett, David Mba

Erschienen in: Design and Modeling of Mechanical Systems—III

Verlag: Springer International Publishing

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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 modeling 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.

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Titel: Canonical Variable Analysis for Fault Detection, System Identification and Performance Estimation
verfasst von: Xiaochuan Li
Fang Duan
Tariq Sattar
Ian Bennett
David Mba
Verlag: Springer International Publishing
Buch: Design and Modeling of Mechanical Systems—III
Print ISBN: 978-3-319-66696-9

Electronic ISBN: 978-3-319-66697-6

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-66697-6_25

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