An Adaptive Bayesian Melding Method for Reliability Evaluation Via Limited Failure Data: An Application to the Servo Turret

Sun, Bo; Yang, Zhaojun; Chen, Chuanhai; Tian, Hailong; Luo, Wei

doi:10.3390/app10217591

Cited by 4 publications

(3 citation statements)

References 48 publications

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“…Bayesian Network (BN) is a probabilistic graph model that can effectively solve various uncertainty problems [6]. It has been introduced into the reliability analysis of complex systems and has achieved successful applications, such as subsea production systems [7][8][9], electronic systems [10,11], manufacturing systems [12][13][14], computer numerical control machine tools [15,16], wind turbines [17,18]. However, the traditional BN can only carry out static analysis, and it is difficult to model the transition process among multiple states.…”

Section: Introductionmentioning

confidence: 99%

Air conditioning reliability analysis based on dynamic Bayesian network and Markov model

Xu,

Wang,

Zhou

et al. 2024

Int. J. Metrol. Qual. Eng.

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With the popularization of the air conditioning, its reliability during operation has gradually become a focus of attention. However, due to the uncertainty in the reliability analysis process, the accuracy of the results will be affected. To overcome this challenge, a method for air conditioner reliability analysis combining Dynamic Bayesian Network (DBN) and Markov Model (MM) is proposed. Firstly, orthogonal defect classification (ODC) is used to statistic and analyze the defect data of the air conditioning system, and the network structure of the DBN is determined based on the results of the analysis. Then, the state transfer probability of each node is obtained by MM, and then the reliability, steady state availability, and maintainability of the air conditioning system are analyzed. Finally, the effectiveness of the method is verified by a case study of air conditioning failure data. The results show that the steady state availability of the air conditioning system in this case is 0.996.

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Section: Introductionmentioning

confidence: 99%

Air conditioning reliability analysis based on dynamic Bayesian network and Markov model

Xu,

Wang,

Zhou

et al. 2024

Int. J. Metrol. Qual. Eng.

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show abstract

“…The core of a data fusion method is to fuse two prior pieces of information into a coherent prior function. A pooling fusion method is often used to complete the fusion through weight coefficients [15] . A weight coefficient can represent the contribution of different prior information, that is, different data, to the estimation results of the valve-controlled cylinder system model parameters.…”

Section: Introductionmentioning

confidence: 99%

“…5.In Fig.5, prior1, prior2, and prior3 are obtained using the MCMC sampling method based on the Bayesian posterior estimation in Equation(13). The Bayesian posterior estimation expression in Step 4 is updated to Equation(15). The estimation results of the system model parameters (θj1, θj2) and weight coefficient k are also obtained through the MCMC sampling method.…”

mentioning

confidence: 99%

Parameter Estimation of a Valve-Controlled Cylinder System Model Based on Bench Test and Operating Data Fusion

Wang

Lin

et al. 2023

Preprint

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The accurate estimation of parameters is the premise for establishing a high-fidelity simulation model of a valve-controlled cylinder system. Although bench test data are convenient to obtain in model parameter estimation, there is a need for the load data to conform to the actual working conditions. Although the operating data include the actual load information, it is not easy to collect the control valve operating data. This paper proposes a model parameter estimation method based on bench test and operating data fusion to solve the above problems. The proposed method is based on Bayesian theory, and its core is a pool fusion of prior information from bench test and operating data. First, a system model is established, and the parameters in the model are analysed. Then, the bench test and operating data of the system are collected, and the model parameters and weight coefficients are estimated using the data fusion method. Finally, the estimated effects of the data fusion method, Bayesian method, and PSO algorithm on system model parameters are compared. The research shows that the parameter estimation result based on the data fusion method is accurate. The weight coefficient represents the contribution of different prior information to the parameter estimation result. The effect of parameter estimation based on the data fusion method is better than that of the Bayesian method and the PSO algorithm. The more complex the load is, the worse the model's accuracy, which verifies the influence of the load on the valve-controlled cylinder system model and proves that the data fusion method plays an essential role in parameter estimation studies.

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Developing a community responsive resilient contractor selection model for post-disaster reconstruction projects: a build back better approach

Demirci,

Isik

2024

ECAM

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PurposeThis study aims to develop a resilient contractor selection model that also considers community concerns in post-disaster reconstruction projects using a “build-back better (BBB)” approach.Design/methodology/approachAt the outset, a comprehensive literature review was conducted to develop the model. During this literature review, a set of contractor selection criteria were identified. Focus group discussion (FGD) sessions were then organized to revise and refine the identified criteria. As a result, a final list of 39 selection criteria was developed. Next, the Bayesian best worst method (BBWM) was conducted to determine the weights of each selection criterion. Finally, the proposed model was validated by conducting a hypothetical case study and fuzzy VlseKriterijuska Optimizacija I Komoromisno Resenje (fuzzy VIKOR) analysis. Moreover, a sensitivity analysis was conducted to examine the effect of the maximum group utility (v) value in the model.FindingsThe results emphasized that contractors in post-disaster reconstruction projects cannot be selected merely based on “technical” and “economic” factors. Instead, the other aspects, namely “organizational and managerial,” “environmental sustainability,” “resource management and procurement” and “end-user,” should also be taken into consideration. Second, factors related to financial strength, experience, risk management, energy efficiency, resource availability and assurance were particularly deemed the most significant, needing the detailed attention of the decision-makers. Lastly, the case study revealed that the proposed model has a significant potential to systemize contractor selection processes in post-disaster reconstruction projects.Originality/valueDisasters, which are increasing in frequency and severity today, devastate the community’s environmental, economic and social conditions. Post-disaster reconstruction projects commence rapidly after a disaster for recovery. However, poor management procedures in these projects trigger time and cost overruns. Unlike traditional construction projects, post-disaster reconstruction projects are distinctive due to the uncertainties caused by the disasters and the prioritization of community concerns. Therefore, contractors should be selected in a way that can meet the specific needs of reconstruction projects. Otherwise, resilience in the built environment and community satisfaction can hardly be enhanced. Accordingly, this study is one of the first attempts to develop a contractor selection model for post-disaster reconstruction projects. It is believed that the developed model will make essential theoretical and practical contributions to the post-disaster reconstruction domain.

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An Adaptive Bayesian Melding Method for Reliability Evaluation Via Limited Failure Data: An Application to the Servo Turret

Cited by 4 publications

References 48 publications

Air conditioning reliability analysis based on dynamic Bayesian network and Markov model

Air conditioning reliability analysis based on dynamic Bayesian network and Markov model

Parameter Estimation of a Valve-Controlled Cylinder System Model Based on Bench Test and Operating Data Fusion

Developing a community responsive resilient contractor selection model for post-disaster reconstruction projects: a build back better approach

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