Multi-dimensional T-S dynamic fault tree analysis method involving failure correlation

What is it about?

The lack of effective failure correlation analysis is one main reason for the gap between the reliability models and the actual complex systems with mixed static and dynamic characteristics. Takagi and Sugeno (T-S) dynamic fault tree is one powerful tool to analyze the static and dynamic failure logic relationship but it assumes the failure probability of the event is independent. Therefore, this paper proposes a multi-dimensional T-S dynamic fault tree analysis method involving failure correlation. The method integrates the failure probability distribution function of basic events with multi-factors and the multi-dimensional copula function, and the important measure of this method is also deduced. The reliability model expression for systems with failure correlations, both in series and in parallel, is discussed and verified. Compare the proposed method with the assumption that the probability of a failure event is independent. This method solves the problem of a large error when ignoring the failure correlation between parts and the degree of the correlation between variables can be characterized. The reliability analysis can be conducted on complex systems affected both by multi-factors and failure correlations. The proposed method is applied to the reliability analysis of a hydraulic height adjustment system and the correctness and superiority of the method are verified.

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Photo by David Birozy on Unsplash

Photo by David Birozy on Unsplash

Why is it important?

We propose a multi-dimensional T-S dynamic fault tree analysis method involving failure correlation. The method is applied to the hydraulic height adjustment system of a coal mining machine. The system failure probability distribution and basic events importance order of multi-dimensional T-S DFTA are calculated without and with considering failure correlation. The results illustrate that the method is more in line with the actual situation and solves the problem of large errors when there is a correlation between faults. It lays a theoretical basis for discovering the weak links of the system and enhancing the reliability of the system.

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