Bayesian Diagnosability and Active Fault Identification

2025/09/04 by Jay W. McMahon, Kong, Chun-Wei, Morteza Lahijanian +2
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #FOS: Electrical engineering #Fault Detection and Control Systems #Machine Fault Diagnosis Techniques #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2509.04708

openalex publication_date 2025/09/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We study fault identification in discrete-time nonlinear systems subject to additive Gaussian white noise. We introduce a Bayesian framework that explicitly accounts for unmodeled faults under reasonable assumptions. Our approach hinges on a new quantitative diagnosability definition, revealing when passive fault identification (FID) is fundamentally limited by the given control sequence. To overcome such limitations, we propose an active FID strategy that designs control inputs for better fault identification. Numerical studies on a two-water tank system and a Mars satellite with complex and discontinuous dynamics demonstrate that our method significantly reduces failure rates with shorter identification delays compared to purely passive techniques.

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