Knowledge acquisition development in failure diagnosis analysis as an interactive approach

Abstract

Safety and reliability analysis is an important issue to prevent an event which may to occurrence of catastrophic accident in process industries. In this context, conventional safety and reliability assessment technique like as fault tree analysis have been widely used in this regards; however, they still suffer in subjective uncertainty processing and dynamic structure representation which are important in risk assessment procedure. In this paper, a new framework based on 2-tuple intuitionistic fuzzy numbers and Bayesian network mechanism is proposed to evaluate system reliability, to deal with mentioned drawbacks, and to recognize the most critical system components which affects the system reliability. The reliability and safety guarantee of such system in the aspect of continuity operations and enhancing the safety of operators and vehicle drivers are crucial. The results revealed that the proposed model could be useful for diagnosing the systems’ faults compared with listing approaches of safety and reliability analysis.