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Generalized-Likelihood-Ratio (GLR) method is well-known for its most rapid failure detection speed and its easier implementation for the systems whose mathematical models are known. But, it has a drawback such that we must assume several hypotheses a priori which are able to model actual anomalies and failures well. The step hypothesis, which models unexpected anomaly vector function appearing in the system by a step function, has often been used for its convenience. But, we cannot always expect to have such a successful situation where the step hypothesis can be well applied. Thus, we previously proposed a GLR method based on the construction of a tracking function space so that any anomaly function due to dynamics or sensor faults is easily tracked and estimated. The present paper proposes another convenient detection method also in the framework of the GLR technique. It is shown that the proposed method can detect any anomaly easily by recognizing the pattern of the curve of the maximum GLR which is calculated by the conventional step-hypothesized GLR method. This means that we introduce a concept of robustness into the step-hypothesized detection scheme. Because in actual systems the anomaly vector function cannot always be well modeled by a single step function, despite its appearance in the system equations constantly after the anomaly.

Fault Detection of Linear Discrete Dynamic Systems by a Generalized-Likelihood-Ratio Method Using Pattern Recognition Technique