Collaborative data-driven reliability analysis of multi-state fault trees

Abstract

Fault tree modeling and failure analysis of systems that are equipped with sensors and meters are becoming more automated and less human-dependent. For a single system to benefit from its own collected data, it will need to wait for a long time to collect sufficient data to build representative models to increase its reliability. Therefore, if multiple systems with similar functionalities cooperate, the resolution of the collected data will increase. This leads to extracting fault trees with higher accuracy in failure detection and prediction. In this paper, we present an extended approach for collaborative Data-Driven Fault Tree Analysis (DDFTA) of a system which extracts repairable fault trees from time series data streaming from multiple systems/machines sharing similar functionalities. Results are analyzed to estimate the system’s reliability measures and investigate the effect of number of machines cooperating in data collection. Our method is not limited to binary (two states) components, nor to exponential distributions. Results show that applying collaborative data analytics significantly increases the accuracy of data-driven fault tree analysis, specifically for systems following nonexponential distributions.

Dynamic Reliability Assessment Method for a Pantograph System Based on a Multistate T-S Fault Tree, Dynamic Bayesian

“…Rania A. et al [7] Introduced the concept of Dynamic fault tree (DFT) by defining additional gates (called dynamic gates) on the traditional fault tree, which overcame the shortcomings of the traditional static fault tree in not being able to adequately simulate the dynamic failures of a complex system and effectively assessed the reliability of real complex systems. In [8], an extended approach for collaborative data-driven fault tree analysis (DDFTA) of a system is presented, which extracts repairable fault trees from time series data streaming from multiple systems/machines sharing similar functionalities. This method is not limited to binary (two states) components nor to exponential distributions.…”

Proxel-Based Simulation of Fault Trees in R

“…If the system and its components either completely function or fail, reliability analysis for this system has a binary perspective. Multi-state fault trees have the same structure of regular fault trees, but the components or the system may have more than two functioning levels (Lisnianski and Levitin 2003;Niloofar and Lazarova-Molnar 2022).…”