Strategies for handling common cause failures in complex safety instrumented systems. An example to illustrate different methods

International audienceCommon cause failures (CCFs) are an important part of reliability analysis when working with safety instrumented systems (SIS), and engineers have been aware of these types of failures since the midseventies (Fleming, 1974). The purpose of this paper is to develop a strategy for analyzing CCFs by studying an example of an oil-pressure system. This paper presents an example which the standard beta-factor model is unable to describe properly. The focus is given to the following three methods, the beta-factor model, the PDS method, and Markov analysis. The need for Markov analysis becomes evident when working with SIS of a more complex nature, for instance non-identical components. This method is, when using a computer, not overly complex and it is possible to model virtually every possible architecture a SIS may have. Finally, it is always important to remember that if there exists any feedback data or expert knowledge describing the distribution of the number of components that fail in a CCF, this is vital in deciding the most descriptive CCF model. By the term descriptive model, we mean a model that both describes the architecture of the system as accurately as possible, and also makes as few assumptions as possible. It is difficult to assess the quality of the results (since feedback data is missing), so faith is put into the model which mathematically resembles the SIS the most