Application of Bayesian Networks in Reliability Evaluation

Abstract

The Bayesian network (BN) is a powerful model for probabilistic knowledge representation and inference and is increasingly used in the field of reliability evaluation. This paper presents a bibliographic review of BNs that have been proposed for reliability evaluation in the last decades. Studies are classified from the perspective of the objects of reliability evaluation, i.e., hardware, structures, software, and humans. For each classification, the construction and validation of a BN-based reliability model are emphasized. The general procedural steps for BN-based reliability evaluation, including BN structure modeling, BN parameter modeling, BN inference, and model verification and validation, are investigated. Current gaps and challenges in reliability evaluation with BNs are explored, and a few upcoming research directions that are of interest to reliability researchers are identified.

Formal Safety Assessment of a Marine Seismic Survey Vessel Operation, Incorporating Risk Matrix and Fault Tree Analysis

“…The above idea of utilising a single value risk probability estimate in risk matrix and FTA has been criticised in the past, but recently, in a marine risk assessment study, authors found out that the calculated fuzzy risk values were consistent with the results of a single value estimate using the risk matrix technique (Abdussamie et al 2018). Despite this finding, a number of alternative representation structures to deal with the probabilistic representation of epistemic uncertainty are presented, but not discussed herein; they are the following: Bayesian network (Cai et al 2019), evidence theory (Curcurù et al 2012), fuzzy set theory (Komal 2015), possibility theory (Roger Flage et al 2013), and interval analysis (Oliveira et al 2016).…”

Risk Analysis of Urban Dirty Bomb Attacking Based on Bayesian Network

“…Bayesian network was also used to analyze the usual causes of failures and the dependencies among the variables in dust explosion scenarios [19]; Similarly, it turned out to be effective that the scenario analysis was combined with Bayesian network to evaluate the occurrence probability of mine water inrush accident and the hazard evolution, performing the disaster response [20]. Bayesian network can also construct and verify the reliability of the model and be used in the procedures of automatic creation of conditional probability tables [21,22]. Furthermore, Fu et al proposed a method that combined Bayesian network with the principle of case suitability to make early warning on terrorist attacks [23].…”

Bearing Fault Diagnosis Using an Extended Variable Structure Feedback Linearization Observer

“…Various signature analysis methods of vibration measurements have been explored to improve the performance and reliability of the condition monitoring techniques [6,7,8]. Moreover, fault detection and diagnosis can be divided into four major categories: (a) Signal-based [7,11,12,13,14,15], knowledge-based [16,17,18,19,20,21], model-based [22,23,24], and hybrid-based fault diagnosis [12,25,26]. To improve these methods, wavelet analysis has been introduced [15].…”

“…To address this issue, statistical features extracted from the signals and machine learning algorithms, such as support vector machine (SVM) and proximal support vector machine [16], have been used in the literature. Recently, several deep learning techniques such as deep autoencoders [17], artificial neural networks (ANNs), and hierarchical convolution networks have been introduced by various researchers for signal-based FDD [18,19,20,21]. Meanwhile, the diagnosis decision in the knowledge-based approach is fully dependent on the data and on proper tuning, using the various hyper-parameters [28].…”