Reliability analysis and updating of deteriorating systems with dynamic Bayesian networks

Luque, Jesus; Straub, Daniel

doi:10.1016/j.strusafe.2016.03.004

Cited by 105 publications

(53 citation statements)

References 54 publications

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“…If this methodology is developed further, the goal of investigations seems to be, in particular, application of Bayesian networks in the computational model [12,13], which describes propagation of fatigue cracks in the system. This contribution has been developed as a part of the research project GACR 17-01589S "Advanced computational and probabilistic modelling of steel structures taking account fatigue damage" supported by the Czech Grant Agency and also has been completed thanks to the financial support provided to VSB-Technical University of Ostrava by the Czech Ministry of Education, Youth and Sports from the budget for conceptual development of science, research and innovations for the 2017 year.…”

Section: Resultsmentioning

confidence: 99%

Using DOProC method in reliability assessment of steel elements exposed to fatigue

et al. 2017

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Abstract. Fatigue crack damage depends on a number of stress range cycles. This is a time factor in the course of reliability for the entire designed service life. Three sizes are important for the characteristics of the propagation of fatigue cracks -initial size, detectable size and acceptable size. The theoretical model of fatigue crack progression can be based on a linear fracture mechanic. Depending on location of an initial crack, the crack may propagate in structural element e.g. from the edge or from the surface. When determining the required degree of reliability, it is possible to specify the time of the first inspection of the construction which will focus on the fatigue damage. Using a conditional probability and Bayesian approach, times for subsequent inspections can be determined. For probabilistic modelling of fatigue crack progression was used the original and new probabilistic method -the Direct Optimized Probabilistic Calculation ("DOProC"), which uses a purely numerical approach without any simulation techniques or approximation approach based on optimized numerical integration.

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Section: Resultsmentioning

confidence: 99%

Using DOProC method in reliability assessment of steel elements exposed to fatigue

et al. 2017

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“…The POMDP‐based assessment of the VoI presented in this paper assumes that each component behaves independently, and information collected on one component is only relevant for the O&M of that component. Including system‐level interaction and statistical interdependence among components poses highly computational challenges: Attempts to address these challenges can be found in Memarzadeh et al and Luque and Straub …”

Section: Discussionmentioning

confidence: 99%

What makes long‐term monitoring convenient? A parametric analysis of value of information in infrastructure maintenance

Pozzi

2019

Struct Control Health Monit

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Summary Information collected by monitoring systems can provide a significant economic benefit to the operation and maintenance of infrastructure components only under specific conditions. The information has to be precise, not redundant, related to relevant decision problems under uncertainty as, for example, the appropriate scheduling of maintenance actions, and the decision maker needs to be able to process that information and react timely. All these considerations can be naturally embedded in the value of information (VoI), a utility‐based metric for assessing the impact of the additional information in decision making under uncertainty. In this paper, we investigate the relation between the VoI and key features of the monitoring system, of the component deterioration and of the decision‐making process, including measure accuracy and availability, deterioration rates, damage predictability, reaction time, maintenance costs, and the economic discount factor. By leveraging previous work, we model the maintenance process as a partially observable Markov decision process, and we compute the VoI of long‐term monitoring. Our proposed framework allows for a detailed quantitative analysis on the joint effects of these features and can be useful to identify conditions when the benefit of monitoring is high, to assign priorities among components that deserve to be instrumented or to optimize the allocation of resources to monitoring efforts.

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“…In this paper, we adopt a dynamic Bayesian network model described in (Straub 2009, Luque and Straub 2016, Bismut et al 2017 to integrate the SHM into the structural system model with interdependent components. The I&M strategy optimization is performed with a heuristic approach Straub 2017, Bismut et al 2017), with and without SHM, to obtain the VoI of the SHM.…”

Section: The Added Value Of Structural Health Monitoringmentioning

confidence: 99%

“…Hierarchical DBN model of the deterioration process of the Zayas frame. The components' interdependence is modelled through the hyper-parameters α (adapted from Luque and Straub (2016))…”

Section: Dbn Modelmentioning

confidence: 99%

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Inspection and Maintenance Planning in Large Monitored Structures

Bismut¹,

Straub²

2018

Proceedings of the 6th International Symposium on Reliability Engineering and Risk Management

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Structural health monitoring (SHM) systems have become a popular technology to collect information about the condition of structural assets. SHM is typically paired with traditional onsite inspection and maintenance (I&M). Incorporating SHM information in I&M planning is non-trivial, but is key to an improved prediction of the structural condition and to save costs on I&M. In this paper, we propose a framework to combine SHM information with visual inspections and repair actions. It utilizes a hierarchical dynamic Bayesian network (DBN) to probabilistically model the deterioration in the structural system, including all component interactions. The DBN calculates the evolution of the system probability of failure given the monitoring information and I&M actions. By attributing discounted costs to I&M actions and to the consequences of failure, we calculate the expected life cycle-cost for a given I&M strategy by simulating monitoring and inspection histories. The cost optimization of I&M strategies is then approached with a heuristic approach. Finally, the value of information of the SHM system is calculated by comparing the optimal strategies with and without monitoring system.

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Reliability analysis and updating of deteriorating systems with dynamic Bayesian networks

Cited by 105 publications

References 54 publications

Using DOProC method in reliability assessment of steel elements exposed to fatigue

Using DOProC method in reliability assessment of steel elements exposed to fatigue

What makes long‐term monitoring convenient? A parametric analysis of value of information in infrastructure maintenance

Inspection and Maintenance Planning in Large Monitored Structures

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