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The application of high technology to the engineering system has significantly improved the performance of modern systems and at the same time greatly increased the complexity of the systems structure. Manufacture cost of these systems is too high. Once these systems fail, it will cause a great loss. Therefore, it will be extremely important to establish a fault diagnosis model based on their unique fault characteristics and develop a dynamic diagnosis strategy which can locate the fault component quickly and reduce the maintenance cost when these systems break down. Usually, fault diagnosis requires a large amount of historical fault data. However, in engineering practice, application of redundant technologies have improved the reliability of these systems, which raises some challenges in fault diagnosis. For one thing, the behaviours of components in these systems, such as failure priority, functional dependent failures, and sequentially dependent failures should be taken into account. For another, high reliability makes it extremely difficult to obtain complete fault data because these systems may still be in the early life cycle, which results in the epistemic uncertainty. Aiming at these challenges, many researchers have put forward a large number of efficient fault diagnosis methods over the last few decades. Johnson presented a sequential diagnostic method based on heuristic information search [10], which constructed a sequential test procedure to locate the failure using information theory. However, the diagnostic result was not satisfied. A novel diagnosis strategy for multi-value attribute system was proposed based on rollout algorithm, and it obtained an optimal diagnostic sequence [9]. Based on these researches, Tian et al. proposed a fault diagnostic strategy of mulJing Li Rongxing DuAn

Dynamic diagnostic strategy based on reliability analysis and distance-based VIKOR with heterogeneous information