Evaluation of control system reliability using combined dynamic fault trees and Markov models

In this paper, dynamic simulation methods for reliability evaluation of common industry-based control system architectures are investigated. Control system design often employs complex reliability structures in the forms of several levels of software and hardware redundancies, hot and cold standby systems. This is required in order to achieve certain plant availability and safety functions. Control system maintenance requires expert knowledge due to the complexity of troubleshooting steps involved with a hardware or software failures of a large system. Hence, it is crucial to understand the effect of recovery time on reliability and on overall availability in a critical control system. Dynamic Fault Tree Analysis (DFTA), Markov Chains and Reliability Block Diagrams (RBD) are presented and a block library is introduced for addressing the aforementioned modelling problems. In order to be able to evaluate dynamic fault trees and Markov Chains, Monte Carlo simulation has been used. An industry-based case study is presented, where critical failures of a redundant Programmable Logic Controller (PLC) system are identified by a Failure Mode and Effect Analysis (FMEA). The bottom up process of modelling control system reliability is discussed.