Dynamic Bayesian network model for comprehensive risk analysis of fatigue-critical structural details

Dooyoul Lee and Kybeom Kwon

Reliability Engineering and System Safety, 2023, vol. 229, issue C

Abstract: Dynamic Bayesian network (DBN) models are widely used for structural risk analysis because of their powerful parameter learning ability and capability of simplifying the problem by parsing it using nodes and arcs. However, these models update the reliability after inspection and maintenance (I&M) in a manner different than that specified by the aircraft structural integrity program (ASIP). In this study, a DBN model is developed to correctly represent the ASIP method. The model updates the crack length distribution after I&M based on the nondestructive testing (NDT) reliability and repair crack length distribution. The nodes for inequality and equality data are explicitly represented in the DBN, which correspond to the crack length vs. signal amplitude and noise characteristics of the NDT system. The proposed model overcomes drawbacks of existing models—initial overestimation and final underestimation—by appropriately considering the repair crack length distribution. Specifically, a decision node is used, which records the fraction of the crack length distribution removed after inspection. Furthermore, a method for constructing conditional probability tables is presented. The proposed model is applied to the in-service fatigue problem for a J85 engine compressor rotor blade. The findings demonstrate that the proposed model can be used in a wide range of applications.

Keywords: Bayesian Network; Fatigue; Inspection; Maintenance; Repair (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:229:y:2023:i:c:s0951832022004525

DOI: 10.1016/j.ress.2022.108834

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