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Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults

1
Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK
2
Laboratoire Génie Electrique et Électronique de Paris (GeePs), CNRS, CentraleSupélec, Université Paris-Sud, 91190 Gif Sur Yvette, France
3
Laboratoire des Signaux et Systèmes (L2S), CNRS, CentraleSupélec, Université Paris-Sud, 91192 Gif Sur Yvette, France
*
Author to whom correspondence should be addressed.
Energies 2019, 12(4), 726; https://doi.org/10.3390/en12040726
Received: 17 January 2019 / Revised: 12 February 2019 / Accepted: 18 February 2019 / Published: 22 February 2019
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control)
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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. View Full-Text
Keywords: slowly evolving faults; fault detection; fault identification slowly evolving faults; fault detection; fault identification
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Li, X.; Mba, D.; Diallo, D.; Delpha, C. Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults. Energies 2019, 12, 726.

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