scholarly journals A Two-Dimension Dynamic Bayesian Network for Large-Scale Degradation Modeling with an Application to a Bridges Network

2017 ◽  
Vol 32 (8) ◽  
pp. 641-656 ◽  
Author(s):  
Alex Kosgodagan-Dalla Torre ◽  
Thomas G. Yeung ◽  
Oswaldo Morales-Nápoles ◽  
Bruno Castanier ◽  
Johan Maljaars ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Large Scale ◽  
Dynamic Bayesian Network ◽  
Two Dimension ◽  
Degradation Modeling

scholarly journals A Dynamic Bayesian Network for Vehicle Maneuver Prediction in Highway Driving Scenarios: Framework and Verification

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 40 ◽  
Author(s):  
Junxiang Li ◽  
Bin Dai ◽  
Xiaohui Li ◽  
Xin Xu ◽  
Daxue Liu
Keyword(s):  
Bayesian Network ◽  
High Performance ◽  
Large Scale ◽  
Low Cost ◽  
Dynamic Bayesian Network ◽  
Intelligent Vehicles ◽  
Prediction Time ◽  
Vehicle Maneuver ◽  
Prediction Approach ◽  
Real Traffic

Accurate maneuver prediction for surrounding vehicles enables intelligent vehicles to make safe and socially compliant decisions in advance, thus improving the safety and comfort of the driving. The main contribution of this paper is proposing a practical, high-performance, and low-cost maneuver-prediction approach for intelligent vehicles. Our approach is based on a dynamic Bayesian network, which exploits multiple predictive features, namely, historical states of predicting vehicles, road structures, as well as traffic interactions for inferring the probability of each maneuver. The paper also presents algorithms of feature extraction for the network. Our approach is verified on real traffic data in large-scale publicly available datasets. The results show that our approach can recognize the lane-change maneuvers with an F1 score of 80% and an advanced prediction time of 3.75 s, which greatly improves the performance on prediction compared to other baseline approaches.

scholarly journals A dynamic Bayesian network approach for prognosis computations on discrete state systems

2017 ◽  
Vol 231 (5) ◽  
pp. 516-533 ◽  
Author(s):  
Josquin Foulliaron ◽  
Laurent Bouillaut ◽  
Patrice Aknin ◽  
Anne Barros
Keyword(s):  
Complex Systems ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Multicomponent System ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Discrete State ◽  
Virtual Maintenance

The maintenance optimization of complex systems is a key question. One important objective is to be able to anticipate future maintenance actions required to optimize the logistic and future investments. That is why, over the past few years, the predictive maintenance approaches have been an expanding area of research. They rely on the concept of prognosis. Many papers have shown how dynamic Bayesian networks can be relevant to represent multicomponent complex systems and carry out reliability studies. The diagnosis and maintenance group from French institute of science and technology for transport, development and networks (IFSTTAR) developed a model (VirMaLab: Virtual Maintenance Laboratory) based on dynamic Bayesian networks in order to model a multicomponent system with its degradation dynamic and its diagnosis and maintenance processes. Its main purpose is to model a maintenance policy to be able to optimize the maintenance parameters due to the use of dynamic Bayesian networks. A discrete state-space system is considered, periodically observable through a diagnosis process. Such systems are common in railway or road infrastructure fields. This article presents a prognosis algorithm whose purpose is to compute the remaining useful life of the system and update this estimation each time a new diagnosis is available. Then, a representation of this algorithm is given as a dynamic Bayesian network in order to be next integrated into the Virtual Maintenance Laboratory model to include the set of predictive maintenance policies. Inference computation questions on the considered dynamic Bayesian networks will be discussed. Finally, an application on simulated data will be presented.

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