Automating Reliability Analysis: Data-driven Learning and Analysis of Multistate Fault Trees

Lazarova-Molnar, Sanja; Niloofar, Parisa; Barta, Gabor Kevin

doi:10.3850/978-981-14-8593-0_4602-cd

Cited by 10 publications

(5 citation statements)

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“…However, we consider different type of data that is time series data of faults. In a different work by Lazarova-Molnar et al 7 they consider timed data for components with more than two states and provide a complete solution to calculate reliability measures from observational data.…”

Section: Background and Related Workmentioning

confidence: 99%

“…First, time series data recorded from n systems/machines are combined and converted to a truth table with time steps. Then, we follow our previous work, 7,22 and apply DDFTA algorithm that comprises of two main steps: (1) structure learning, or the qualitative analysis part and (2) quantitative analysis based on the output of the first step. In the structure learning step, we extract the minimal cut sets from the time series data set collected from multiple systems, and then use Boolean algebra to build a fault tree that aims to be mathematically identical to the true fault tree of the system.…”

Section: Collaborative Data-driven Reliability Analysismentioning

confidence: 99%

“…In this paper, we extend DDFTA method presented in our previous work 7 to enable collaborative Data-Driven Fault Tree Analysis (DDFTA) of a multi-state system which extracts repairable fault trees from time series data coming from multiple machines sharing the same functionalities. Results are then analyzed to estimate reliability measures and investigate the effect of number of machines cooperating in data collection.…”

Section: Introductionmentioning

confidence: 99%

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

Niloofar

Lazarova-Molnar

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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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.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Collaborative Data-driven Reliability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Niloofar

Lazarova-Molnar

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…More advanced tools are also used, including deep learning methods [9,38,10,53,77], based on Monte Carlo simulation [46,21] as well as kriging and first-order reliability [48]. Perti nets [17], Fault tree analysis (FTA) [34], and the load duration distribution method (LDD) [47] are also used. Particularly popular are also time series methods, including e.g.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the maintenance system readiness using the semi-Markov model taking into account hidden factors

Kozłowski,

Borucka,

Oleszczuk

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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Modelling the time that the system remains in a given state using classical distributions is not always possible. In many cases, empirical distributions are multimodal due to the influence of external, hidden factors and the selection of the best classical distributions may lead to erroneous results. In the article the method of diagnosis of influence of hidden factors into sojourn time of semi-Markov models was presented. In order to capture hidden factors, the authors proposed to model the distributions of the sojourn time with a mixture of distributions, which is a significant novelty in relation to the studies presented in the literature. Hidden factors directly affect the reliability of technical systems. Detecting the existence of these factors enables more accurate modeling of system readiness. Paying attention to irregularities caused by hidden factors makes it possible to reduce system maintenance costs. Such a system model provides complete information and enables a reliable assessment of the system readiness and maintenance.

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“…Rare events and the thereby reached system states are guaranteed to be considered. The method has shown to be applicable for the numerical simulation of stochastic Petri Nets, warranty models and Fault Tree Analysis (FTA) (Lazarova-Molnar et al 2020;Lazarova-Molnar and Horton 2003;Niloofar and Lazarova-Molnar 2021).…”

Section: Introductionmentioning

confidence: 99%

Proxel-Based Simulation of Fault Trees in R

Niloofar

Haghbin

Lazarova-Molnar

2022

2022 Winter Simulation Conference (WSC)

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Simulation is an advantageous solution to calculate reliability of complex systems when analytical methods are not able to deliver results in time or when we do not have the resources to apply analytical methods. Proxel-based simulation has shown to be very efficient in reliability analysis of complex systems. In this paper, we present a package called ftaproxim for proxel-based simulation of complex systems using fault trees, developed in the R programming language. ftaproxim can calculate and plot instantaneous unavailabilities of repairable events and the system as a whole.

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Automating Reliability Analysis: Data-driven Learning and Analysis of Multistate Fault Trees

Cited by 10 publications

References 0 publications

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

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

Evaluation of the maintenance system readiness using the semi-Markov model taking into account hidden factors

Proxel-Based Simulation of Fault Trees in R

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