A Multi-Model Approach to Failure Detection in Uncertain Sampled-Data Systems

In this paper we propose a methodology for active failure detection for sampled-data systems in the presence of model uncertainty. Active failure detection consists of injecting a signal (called an auxiliary signal) into the system. Using a multi-model framework to represent normal and failed behaviors of the system,we develop algorithms for the design of optimal auxiliary signals and online detection tests. The tests give guaranteed detection. Any conservatism inherited from the multi-model framework increases the size of the test signal and is not reflected in missed failures or false alarms as with several other approaches.