Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults

Date

2019-02-22

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

MDPI

Type

Article

Peer reviewed

Yes

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 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.

Description

open access article

Keywords

slowly evolving faults, fault detection, fault identification

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.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)