Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults
Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK
Laboratoire Génie Electrique et Électronique de Paris (GeePs), CNRS, CentraleSupélec, Université Paris-Sud, 91190 Gif Sur Yvette, France
Laboratoire des Signaux et Systèmes (L2S), CNRS, CentraleSupélec, Université Paris-Sud, 91192 Gif Sur Yvette, France
Author to whom correspondence should be addressed.
Received: 17 January 2019 / Revised: 12 February 2019 / Accepted: 18 February 2019 / Published: 22 February 2019
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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
and a CVR-based monitoring index,
. A CVR-based contribution plot approach is also proposed based on
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.
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MDPI and ACS Style
Li, X.; Mba, D.; Diallo, D.; Delpha, C. Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults. Energies 2019, 12, 726.
Li X, Mba D, Diallo D, Delpha C. Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults. Energies. 2019; 12(4):726.
Li, Xiaochuan; Mba, David; Diallo, Demba; Delpha, Claude. 2019. "Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults." Energies 12, no. 4: 726.
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