Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults

Xiaochuan Li, David Mba, Demba Diallo and Claude Delpha
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Xiaochuan Li: Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK
David Mba: Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK
Demba Diallo: Laboratoire Génie Electrique et Électronique de Paris (GeePs), CNRS, CentraleSupélec, Université Paris-Sud, 91190 Gif Sur Yvette, France
Claude Delpha: Laboratoire des Signaux et Systèmes (L2S), CNRS, CentraleSupélec, Université Paris-Sud, 91192 Gif Sur Yvette, France

Energies, 2019, vol. 12, issue 4, 1-16

Abstract: This study puts forward a novel diagnostic approach based on canonical variate residuals (CVR) to implement incipient fault diagnosis for dynamic process monitoring. The conventional canonical variate analysis (CVA) fault detection approach is extended to form a new monitoring index based on Hotelling’s T 2 , Q and a CVR-based monitoring index, T d . A CVR-based contribution plot approach is also proposed based on Q and T d statistics. Two performance metrics: (1) false alarm rate and (2) missed detection rate are used to assess the effectiveness of the proposed approach. The CVR diagnostic approach was validated on incipient faults in a continuous stirred tank reactor (CSTR) system and an operational centrifugal compressor.

Keywords: slowly evolving faults; fault detection; fault identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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