A numerical simulation method for a repairable dynamic fault tree

Abstract

Dynamic fault trees are important tools for modeling systems with sequence failure behaviors. The Markov chain state space method is the only analytical approach for a repairable dynamic fault tree (DFT). However, this method suffers from state space explosion, and is not suitable for analyzing a large scale repairable DFT. Furthermore, the Markov chain state space method requires the components’ time-to-failure to follow exponential distributions, which limits its application. In this study, motivated to efficiently analyze a repairable DFT, a Monte Carlo simulation method based on the coupling of minimal cut sequence set (MCSS) and its sequential failure region (SFR) is proposed. To validate the proposed method, a numerical case was studied. The results demonstrated that our proposed approach was more efficient than other methods and applicable for repairable DFTs with arbitrary time-to-failure distributed components. In contrast to the Markov chain state space method, the proposed method is straightforward, simple and efficient.

Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds

“…Note that the AND gate triggers when both of the input BEs have occurred, while the OR gate triggers when at least one of the input BEs has occurred. In addition to quantitative analysis enabled by the calculation of TE probability, FTA allows for qualitative analysis of the minimal cut sets (MCS), which are the sets containing the minimum number of BEs to ensure the occurrence of a TE [29] (for example, the minimal cut sets for the FT shown in Figure 2 There have been several approaches for quantitative analysis of FT proposed in the literature, such as state-space models [30][31][32], simulation methods [29,33,34], algebraic approaches [35,36], and commercial software tools and model checkers such as Galileo [37], Coral [38], DFTCalc [39], and Storm [40], among others [41]. Although research effort that has been dedicated to FTA, the methodology does have a number of issues [42,43].…”

“…For example, in [46], the authors preprocessed data and ran ML algorithms on edge devices, integrating these features within a modular platform with computational cloud capabilities. In [34,47], the authors exploited the computational capabilities of edge devices to run complex algorithms for fault detection, while in [48] the authors used edge computing for anomaly detection.…”

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