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Recursive filters for a partially observable system subject to random failure. (English) Zbl 1036.60079
A failure-prone system is modeled as a continuous-time Markov chain with finite state-space. The failure state is observable, while the working state can only be estimated based on the information obtained through condition monitoring at discrete time epochs. By using a change of measure approach, a general recursive filter is derived; special cases include filters for state, number of jumps, occupation times, number of transitions from state to observation and occupation times at observation epochs. Up-dated parameter estimates are given and some prediction problems are studied. A numerical example based on a real data set is discussed.
MSC:
60K10Applications of renewal theory
60G35Signal detection and filtering (stochastic processes)
90B25Reliability, availability, maintenance, inspection, etc. (optimization)