Affiliation:
1. University of California at Los Angeles
Abstract
Conventional Shiryayev Multiple Hypothesis Sequential Probability Test (MHSPT) can be applied only to an independent measurement sequence. We extend Shiryayev MHSPT to applications where the measurements are a correlated sequence generated by known dynamics, and is called here the MHSPTC. This test, which serves as a decision rule, is derived from a dynamic programming approach, and can be applied to announce system failure with probability from residuals generated from analytical redundancy methods. The decision policy is determined by considering cost of measurement, false alarm, and miss detection. This test announces failure disruption in minimum time when false alarm and miss detection rates are specified. The MHSPTC is implemented and verified in the health monitoring system of an automated vehicle.
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