Robust Model-Based Fault Diagnosis: The State of the ART

Abstract The robustness issues in model-based fault detection and fault isolation (fault diagnosis) have received considerable attention in recent years, due to the increasing demand for safe and reliable operation of uncertain and complex dynamic systems. The ultimate goal of robustness is to provide rapid and reliable detection and isolation of system faults when the plant under control is disturbed, and when the mathematical model upon which the diagnosis is based cannot faithfully reproduce the full dynamic operation of the plant. The aim of this paper is to review methods for robust fault diagnosis, based principally on residual generation. Some of the key challenges and potential for future directions in the research are drawn up.

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