Small Fault Diagnosis With Gap Metric

Hailang Jin, Zhiqiang Zuo*, Yijing Wang, Lei Cui, Zhengen Zhao, Linlin Li, Zhiwei Gao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


This article proposes a novel data-driven gap metric fault detection and isolation (FDI) approach for small multiplicative fault. First, the scheme of model-based fault classification and gradation is developed by means of the gap metric. Subsequently, the data-driven gap metric is utilized to detect a small fault via the mechanism model. Furthermore, fault detectability criterion is derived with the help of the developed fault detectability indicator. The relationship between fault detectability indicator and fault detection index is then investigated to analyze fault detection performance. To enhance fault isolability, a solution of appropriate fault cluster center model and radius is provided under the condition of fault isolation. Third, a gap metric fault-tolerant control strategy is exploited to guarantee system stability when a large fault is diagnosed by the developed FDI approach. The speed regulation of dc-motor and dc–dc converter are used for simulation and experiment verifications. Moreover, the comparison results and Monte Carlo simulation demonstrate the superiority and reliability of the proposed method.
Original languageEnglish
Pages (from-to)5715-5728
Number of pages14
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number9
Early online date26 May 2023
Publication statusPublished - 1 Sep 2023

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