Reliability analysis and functional design using Bayesian networks generated automatically by an “Idea Algebra” framework

Amrin, Andas; Zarikas, Vasilios; Spitas, Christos

doi:10.1016/j.ress.2018.07.020

Cited by 29 publications

(20 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In both cases, it becomes cumbersome to accommodate any design changes because of the necessity of creating a new model. Recently, Amrin et al [2] introduced a method where BN of a mechanical system is automatically generated from the system's description for reliability analysis.…”

Section: Bayesian Network In Reliability Analysismentioning

confidence: 99%

A Model-Based Reliability Analysis Method Using Bayesian Network

Kabir

Campean

2021

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Bayesian Network (BN)-based methods are increasingly used in system reliability analysis. While BNs enable to perform multiple analyses based on a single model, the construction of robust BN models relies either on the conversion from other intermediate system model structures or direct analyst-led development based on experts input, both requiring significant human effort. This article proposes an architecture model-based approach for the direct generation of a BN model. Given the architectural model of a system, a systematic bottom-up approach is suggested, underpinned by failure behaviour models of components composed based on interaction models to create a system-level failure behaviour model. Interoperability and reusability of models are supported by a library of component failure models. The approach was illustrated with application to a case study of a steam boiler system.

show abstract

Section: Bayesian Network In Reliability Analysismentioning

confidence: 99%

A Model-Based Reliability Analysis Method Using Bayesian Network

Kabir

Campean

2021

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

show abstract

“…In case of discrete random variables, the conditional relationship between the nodes can be represented in terms of Conditional Probability Table (CPT). The construction of CPT is conducted in the way that [38]:…”

Section: Bayesian Networkmentioning

confidence: 99%

Smart Building’s Elevator with Intelligent Control Algorithm based on Bayesian Networks

Bapin

Zarikas²

2019

ijacsa

View full text Add to dashboard Cite

Implementation of the intelligent elevator control systems based on machine-learning algorithms should play an important role in our effort to improve the sustainability and convenience of multi-floor buildings. Traditional elevator control algorithms are not capable of operating efficiently in the presence of uncertainty caused by random flow of people. As opposed to conventional elevator control approach, the proposed algorithm utilizes the information about passenger group sizes and their waiting time, provided by the image acquisition and processing system. Next, this information is used by the probabilistic decision-making model to conduct Bayesian inference and update the variable parameters. The proposed algorithm utilizes the variable elimination technique to reduce the computational complexity associated with calculation of marginal and conditional probabilities, and Expectation-Maximization algorithm to ensure the completeness of the data sets. The proposed algorithm was evaluated by assessing the correspondence level of the resulting decisions with expected ones. Significant improvement in correspondence level was obtained by adjusting the probability distributions of the variables affecting the decision-making process. The aim was to construct a decision engine capable to control the elevators actions, in way that improves user's satisfaction. Both sensitivity analysis and evaluation study of the implemented model, according to several scenarios, are presented. The overall algorithm proved to exhibit the desired behavior, in 94% case of the scenarios tested.

show abstract

“…BNs have already been used in a variety of different engineering fields (Bapin and Zarikas, 2019a;Amrin et al, 2018;Zarikas et al, 2018;Papanikolaou et al, 2017;Zarikas et al, 2015;Zarikas et al, 2013;Zarikas, 2007;Stephanidis et al, 2005;Yang et al, 2019;Gao and Dong, 2019;Jha et al, 2019;Tselykh et al, 2018;Nakasima-L opez et al, 2018;Ung, 2018;Quinn et al, 2017;Yang and Xu, 2017;Sanchez et al, 2017), yet only a few works use BNs for the power system reliability assessment or probabilistic quantification of power generating reserve capacity. Yu et al (1999) is one of the first works, which uses BNs to evaluate reliability of a power grid.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic estimation of spinning reserves in smart grids with Bayesian-driven reserve allocation adjustment algorithm

Bapin

Zarikas

2021

IJESM

View full text Add to dashboard Cite

Purpose This study aims to introduce a methodology for optimal allocation of spinning reserves taking into account load, wind and solar generation by application of the univariate and bivariate parametric models, conventional intra and inter-zonal spinning reserve capacity as well as demand response through utilization of capacity outage probability tables and the equivalent assisting unit approach. Design/methodology/approach The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern probability density function (PDF). The study also uses the Bayesian network (BN) algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours. Findings The results show that the utilization of bivariate wind prediction model along with reserve allocation adjustment algorithm improve reliability of the power grid by 2.66% and reduce the total system operating costs by 1.12%. Originality/value The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern PDF. The study also uses the BN algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours.

show abstract

Reliability analysis and functional design using Bayesian networks generated automatically by an “Idea Algebra” framework

Cited by 29 publications

References 28 publications

A Model-Based Reliability Analysis Method Using Bayesian Network

A Model-Based Reliability Analysis Method Using Bayesian Network

Smart Building’s Elevator with Intelligent Control Algorithm based on Bayesian Networks

Probabilistic estimation of spinning reserves in smart grids with Bayesian-driven reserve allocation adjustment algorithm

Contact Info

Product

Resources

About