Integration of simulation and Markov Chains to support Bayesian Networks for probabilistic failure analysis of complex systems

Development of failure analysis techniques for complex engineering systems is evolving rapidly. Complexity in these systems refers to the complex interrelations among system components, variables, factors, and parameters as well as the large number of components to include in the study. It is not an easy task to include all interrelationships of a complex system into one representation. New dynamic and uncertain factors affecting engineering systems, like climate change, new technologies, and new uses, make it clear that the water reservoir systems operations and performance are under probabilistic inputs from many different factors. This means that failure of such systems should be assessed using multidisciplinary probabilistic uncertainty measures. Bayesian Networks (BNs) provide a flexible way of representing such complex systems and their interrelating components probabilistically and in a single unified representation. Compared to other techniques such as fault tree and event tree analyses methods, BN is useful in representing complex networks that have multiple events and different types of variables in one representation, with the ability to predict the effects, or diagnose the causes leading to a certain effect. In this paper, two proposed methodologies are developed to support BNs in dealing with the failure analysis of complex engineering systems, i.e. Simulation Supported Bayesian Networks (SSBNs), and Markov Chain Simulation Supported Bayesian Networks (MCSSBNs). For complex networks, whose failures are affected by a large number of uncertain interconnected variables, these proposed methods are used for efficiently predicting failure probabilities. Compared to exhaustive simulation, the new tools have the distinction of decomposing the complex system into many sub-systems, which makes it easier for understanding the network and faster for simulating the entire network while taking multiple operation scenarios into consideration. The efficiency of these techniques is demonstrated through their application to a pilot system of two dam reservoirs, where the results of SSBNs and MCSSBNs are compared with those of the simulation of entire system operations.

Automated summary

The development of failure analysis techniques for complex engineering systems faces challenges due to interrelations and uncertainty. Bayesian Networks provide a flexible way to represent such systems probabilistically. Proposed methodologies such as SSBNs and MCSSBNs support efficient prediction of failure probabilities for complex networks with multiple uncertain interconnected variables.