An integrated safety prognosis model for complex system based on dynamic Bayesian network and ant colony algorithm

In complex industrial system, most of single faults have multiple propagation paths, so any local slight deviation is able to propagate, spread, accumulate and increase through system fault causal chains. It will finally result in unplanned outages and even catastrophic accidents, which lead to huge economic losses, environmental contamination, or human injuries. In order to ensure system intrinsic safety and increase operational performance and reliability in a long period, this study proposes an integrated safety prognosis model (ISPM) considering the randomness, complexity and uncertainty of fault propagation.ISPM is developed based on dynamic Bayesian networks to model the propagation of faults in a complex system, integrating the priori knowledge of the interactions and dependencies among subsystems, components, and the environment of the system, as well as the relationships between fault causes and effects. So the current safety state and potential risk of system can be assessed by locating potential hazard origins and deducing corresponding possible consequences. Furthermore, ISPM is also developed to predict the future degradation trend in terms of future reliability or performance of system, and provide proper proactive maintenance plans. Ant colony algorithm is introduced in ISPM by comprehensively considering two factors as probability and severity of faults, to perform the quantitative risk estimation of the underlining system. The feasibility and benefits of ISPM are investigated with a field case study of gas turbine compressor system. According to the outputs given by ISPM in the application, proactive maintenance, safety-related actions and contingency plans are further discussed and then made to keep the system in a high reliability and safety level in the long term.