Abstract
Temporal fault trees (TFTs), an extension of classical Boolean fault trees, can model time-dependent failure behaviour of dynamic systems. The methodologies used for quantitative analysis of TFTs include algebraic solutions, Petri nets (PN), and Bayesian networks (BN). In these approaches, precise failure data of components are usually used to calculate the probability of the top event of a TFT. However, it can be problematic to obtain these precise data due to the imprecise and incomplete information about the components of a system. In this paper, we propose a framework that combines intuitionistic fuzzy set theory and expert elicitation to enable quantitative analysis of TFTs of dynamic systems with uncertain data. Experts' opinions are taken into account to compute the failure probability of the basic events of the TFT as intuitionistic fuzzy numbers. Subsequently, for the algebraic approach, the intuitionistic fuzzy operators for the logic gates of TFT are defined to quantify the TFT. On the other hand, for the quantification of TFTs via PN and BN-based approaches, the intuitionistic fuzzy numbers are defuzzified to be used in these approaches. As a result, the framework can be used with all the currently available TFT analysis approaches. The effectiveness of the proposed framework is illustrated via application to a practical system and through a comparison of the results of each approach.
Highlights
Over the years, we have seen a widespread use of safety critical systems in a wide variety of industries, including automotive, aerospace, maritime, medical, nuclear, and energy sectors
There exist small differences between the TE probabilities estimated by the different approaches, the important thing to note is that the use of intuitionistic fuzzy set theory with expert elicitation enables the analysis in cases where the available information about system components is insufficient to define their failure rate using classical fuzzy sets
In this paper, we presented a framework for temporal Fault tree analysis (FTA) to evaluate system reliability using intuitionistic fuzzy set theory where failure data for system components are unavailable or insufficient
Summary
We have seen a widespread use of safety critical systems in a wide variety of industries, including automotive, aerospace, maritime, medical, nuclear, and energy sectors. The potential applications of intuitionistic fuzzy set theory in classical static FTA has been investigated in the past, to the best of the authors’ knowledge, it has not yet been investigated how IFS could be used with dynamic extensions of fault trees. We propose a framework for integrating IFS theory with expert elicitation to enable the dynamic reliability analysis of systems through TFTs where exact failure data of system components are unavailable. In this context the contributions of this paper include:.
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