Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults
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 T2, Q and a CVR-based monitoring index, Td. A CVR-based contribution plot approach is also proposed based on Q and Td 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.
open access article
Citation : Li, X., Mba, D., Diallo, D. and Delpha, C. (2019) Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults. Energies, 12(4), p.726.
Research Group : Institute of Artificial Intelligence (IAI)
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes
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