Fault diagnosis for complex systems based on dynamic evidential network and multi-attribute decision making with interval numbers

With the development of science and technology, the functional requirement and modernization level of modern equipments are increasing, which makes these systems become more and more complex and raises some challenges in fault diagnosis. These challenges are shown as follows. (1) Failure dependency of components. Modern engineering systems are becoming increasingly complex, which makes components interact with each other. So, dynamic fault behaviors should be taken into account to construct the fault model. (2) The life distributions of components are different. Modern systems include a variety of components, and they may have different life distributions. Some classical static modeling techniques, including reliability block diagram model [12], fault tree (FT) model [20], and binary decision diagrams (BDD) model [23] have been widely used to model static systems. But these models assume that all components follow the exponential distribution. However, in the practical engineering, different components may have different distributions. For complex systems, a mixed life distribution should be used to analyze these systems. (3) There are a large number of uncertain factors and uncertain information. Many complex systems have adopted a variety of fault tolerant technologies to improve their dependability. However, high reliability makes it difficult to get sufficient fault data. In the case of the small sample data, the traditional methods based on the probability theory are no longer appropriate for complex systems. Aiming at these challenges mentioned above, many efficient diagnostic methods have been proposed. In order to model the dynamic failure characteristics, DFT [6], Markov model [28] and dynamic Bayesian networks (DBN) [9, 26] have been proposed to capture the above mentioned dynamic failure behaviors. DFT is widely used to model the dynamic systems as the extensions of the traditional static fault trees with sequenceand function-dependent failure behaviors. Ge et al. present an improved sequential binary decision diagrams (SBDD) method for highly coupled DFT where different dynamic gates often coexist and interact Rongxing DUAN Longfei HU Yanni LIN