Robust Failure Detection and Isolation
The basic objective of a fault detection and isolation methodology for dynamic systems is to detect failures and to identify, or isolate, the failed component. The most obvious method for automatic fault detection is the use of hardware redundancy, where measurements from multiple sensors are compared, and the existence of a failure is determined by implementing a voting mechanism. In many situations, however, hardware redundancy may not be possible or desirable, since it imposes a penalty in terms of volume, weight, etc. In other situations, such as with actuators, direct measurement is often not possible. In these cases, indirect measurements may be used to infer the component fault status using a model of the system. One method to analytically detect the existence of a failure is to look for anomalies in the plant’s output relative to a model-based estimate of that output. Plant models, however, are generally incomplete and inaccurate. Moreover, these failure detection and isolation algorithms often assume a particular failure mode. These plant dynamics and failure mode modeling errors can either cause a high false alarm rate, or make it difficult to detect failures. Any robust detection and isolation test that is designed to overcome the problems associated with these modeling errors must be able to distinguish between model uncertainties and failures in order to avoid excessive false alarms or missed detections.
KeywordsLikelihood Ratio Test Kalman Filter Decision Function Failure State Underwater Vehicle
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