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Fault tolerant technology is often used to improve systems reliability. However, high reliability makes it difficult to get sufficient fault samples, resulting in epistemic uncertainty, which increases significantly challenges in these systems diagnosis. A novel dynamic diagnosis strategy for complex systems is proposed to improve the diagnostic efficiency in the paper, which makes full use of dynamic fault tree, Bayesian networks (BN), fuzzy sets theory, and TOPSIS. Specifically, it uses a dynamic fault tree to model dynamic fault modes and evaluates the failure rates of the basic events using fuzzy sets to deal with epistemic uncertainty. Furthermore, it generates qualitative structure information based on zero-suppressed binary decision diagrams and calculates quantitative parameters provided by reliability analysis using a hybrid approach. Additionally, sensors data are incorporated to update the qualitative information and quantitative parameters. Qualitative information, quantitative parameters, and previous diagnosis result are taken into account to design a new dynamic diagnosis strategy which can locate the fault at the lowest cost. Finally, a case study is given to verify the developed approach and to demonstrate its effectiveness.

Dynamic Diagnosis Strategy for Redundant Systems Based on Reliability Analysis and Sensors under Epistemic Uncertainty