Integration of simulation and Markov Chains to support Bayesian Networks for probabilistic failure analysis of complex systems

El-Awady, Ahmed; Ponnambalam, K.

doi:10.1016/j.ress.2021.107511

Cited by 24 publications

(11 citation statements)

References 15 publications

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“…NTB was calculated considering the possibility of an independent fault and the possibility of a lower-level fault being assigned by higher-level fault tracing. The calculation method was shown in Equations ( 13)- (15).…”

Section: Phase 4: Output Results Based On Fault Decoupling Numerical ...mentioning

confidence: 99%

“…BN is an important and well-known probabilistic graphical model that decouples fault based on probabilistic information representation and posterior inference rule. 4 During the past decades, 6,[13][14][15][16] this method has been widely used and studied for system reliability analysis and fault diagnosis. However, it is still criticised in a large database, particularly the likelihood function for dynamic application.…”

Section: Introductionmentioning

confidence: 99%

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Physics‐data‐fusion based decoupling model for coupled faults of complex electromechanical systems

Wang

Liang

et al. 2022

Quality & Reliability Eng

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Coupled faults are formed by the nonlinear coupling of multiple lower-level faults in complex electromechanical systems (CES). Although fault decoupling plays a crucial role in locating fault cause and isolating fault components, it still faces challenges due to the harsh reality of common mode failure, networked propagation, and a lack of accurate fault mechanism knowledge in the fault coupling process. A novel physics-data-fusion-based decoupling model for coupled faults of CES was proposed using standard meta components, rigorous formulation, and intuitive representation. First, a hierarchical graph representing the static complex decoupling model was defined by composing proposed meta models. Second, the dynamic model parameters inspired by the time-varying fault characteristics were determined using real-time operation data analysis. Then, based on a proposed numerical reasoning formula, the most likely fault cause was determined, which can also identify fault level by level. Finally, the decoupling model was proved to be reasonable and effective with an offshore wind turbine case. As a graphical modelling method, it handles the decoupling process by fusing static physics and dynamic data of coupled faults. While inheriting the benefits of conventional models, it overcomes the limitations of these existing methods for real-time results. Moreover, the proposed method provided a foundation for tracing the root cause of performance fluctuations, fault detection, and isolation of CES.

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Section: Phase 4: Output Results Based On Fault Decoupling Numerical ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Physics‐data‐fusion based decoupling model for coupled faults of complex electromechanical systems

Wang

Liang

et al. 2022

Quality & Reliability Eng

View full text Add to dashboard Cite

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“…PGM has been used in several applications from medical diagnosis, object recognition to virus evolution modeling (31)(32)(33). Despite limited use in pediatric sepsis, PGM has been investigated in various disease diagnoses, such as cancer, heart disease, and adult sepsis (8,34,35).…”

Section: Discussionmentioning

confidence: 99%

Effective diagnosis of sepsis in critically ill children using probabilistic graphical model

Nguyen¹,

Poh²,

Chong³

et al. 2023

Transl Pediatr

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Background: Probabilistic graphical model, a rich graphical framework in modelling associations between variables in complex domains, can be utilized to aid clinical diagnosis. However, its application in pediatric sepsis remains limited. This study aims to explore the utility of probabilistic graphical models in pediatric sepsis in the pediatric intensive care unit. Methods:We conducted a retrospective study on children using the first 24-hour clinical data of the intensive care unit admission from the Pediatric Intensive Care Dataset, 2010-2019. A probabilistic graphical model method, Tree Augmented Naive Bayes, was used to build diagnosis models using combinations of four categories: vital signs, clinical symptoms, laboratory, and microbiological tests. Variables were reviewed and selected by clinicians. Sepsis cases were identified with the discharged diagnosis of sepsis or suspected infection with the systemic inflammatory response syndrome. Performance was measured by the average sensitivity, specificity, accuracy, and area under the curve of ten-fold cross-validations.Results: We extracted 3,014 admissions [median age of 1.13 (interquartile range: 0.15-4.30) years old].There were 134 (4.4%) and 2,880 (95.6%) sepsis and non-sepsis patients, respectively. All diagnosis models had high accuracy (0.92-0.96), specificity (0.95-0.99), and area under the curve (0.77-0.87). Sensitivity varied with different combinations of variables. The model that combined all four categories yielded the best performance [accuracy: 0.93 (95% confidence interval: 0.916-0.936); sensitivity: 0.46 (95% confidence interval: 0.376-0.550), specificity: 0.95 (95% confidence interval: 0.940-0.956), area under the curve: 0.87 (95% confidence interval: 0.826-0.906)]. Microbiological tests had low sensitivity (<0.10) with high incidence of negative results (67.2%). Conclusions:We demonstrated that the probabilistic graphical model is a feasible diagnostic tool for pediatric sepsis. Future studies using different datasets should be conducted to assess its utility to aid clinicians in the diagnosis of sepsis.

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“…erefore, Bayesian theory combines principles such as graph theory, probability theory, computer science, and statistics, and GMs with undirected edges are often referred to as Markov random elds or Markov networks. ese networks are based on the concept of Markov chains, which provide a simple de nition of independence, that is, between any two di erent nodes [23][24][25][26]. e formulas for calculating the mean relative error MAPE and the root mean square error RMSE are as follows:…”

Section: Network Security Risk Quantificationmentioning

confidence: 99%

Application of Bayesian Algorithm in Risk Quantification for Network Security

Lei

2022

Computational Intelligence and Neuroscience

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Network security risk quantification involves both technical and management aspects. Risk quantification has great uncertainty and cannot be fully quantified. Therefore, the fully objective realization of network information security risk quantification is not yet mature. This paper analyzes and quantifies the network security risks caused by various threat sources through a network security risk quantification model based on the Bayesian algorithm. By combining expert knowledge, the conditional probability matrix under the inference rule of the Bayesian algorithm is clarified, and the subjective judgment information of experts on the damage degree of the target information system is synthesized into the prior information system of network security threat. The Bayesian algorithm is used to realize the observation node of objective assessment information and combining subjective security threat levels to achieve continuity and accumulation of security assessments. The error is about 3%, which has a very good effect on the quantification of network security risk.

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Integration of simulation and Markov Chains to support Bayesian Networks for probabilistic failure analysis of complex systems

Cited by 24 publications

References 15 publications

Physics‐data‐fusion based decoupling model for coupled faults of complex electromechanical systems

Physics‐data‐fusion based decoupling model for coupled faults of complex electromechanical systems

Effective diagnosis of sepsis in critically ill children using probabilistic graphical model

Application of Bayesian Algorithm in Risk Quantification for Network Security

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