scholarly journals Dynamic Bayesian Network-Based Escape Probability Estimation for Coach Fire Accidents

2021 ◽  
Vol 33 (2) ◽  
pp. 193-204
Author(s):  
Chenyu Zhou ◽  
Xuan Zhao ◽  
Qiang Yu ◽  
Rong Huang
Keyword(s):  
Bayesian Network ◽  
Failure Modes ◽  
Dynamic Bayesian Network ◽  
Head Rotation ◽  
Classification And Regression Tree ◽  
Temporal Part ◽  
Escape Training ◽  
Escape Probability ◽  
Effective Measure ◽  
Fire Accidents

Coach emergency escape research is an effective measure to reduce casualties under serious vehicle fire accidents. A novel experiment method employing a wireless transducer was implemented and the head rotation speed, rotation moment and rotation duration were collected as the input variables for the classification and regression tree (CART) model. Based on this model, the classification result explicitly pointed out that the exit searching efficiency was evolving. By ignoring the last three unimportant factors from the Analytic Hierarchy Process (AHP), the ultimate Dynamic Bayesian Network (DBN) was built with the temporal part of the CART output and the time-independent part of the vehicle characteristics. Simulation showed that the most efficient exit searching period is the middle escape stage, which is 10 seconds after the emergency signal is triggered, and the escape probability clearly increases with the efficient exit searching. Furthermore, receiving emergency escape training contributes to a significant escape probability improvement of more than 10%. Compared with different failure modes, the emergency hammer layout and door reliability have a more significant influence on the escape probability improvement than aisle condition. Based on the simulation results, the escape probability will significantly drop below 0.55 if the emergency hammers, door, and aisle are all in a failure state.

Multi-state reliability assessment for hydraulic lifting system based on the theory of dynamic Bayesian networks

2016 ◽  
Vol 230 (6) ◽  
pp. 533-544 ◽  
Author(s):  
Chun Su ◽  
Ning Lin ◽  
Yequn Fu
Keyword(s):  
Bayesian Network ◽  
Failure Modes ◽  
Reliability Assessment ◽  
Mechanical Systems ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Effect Analysis ◽  
Analysis And Design ◽  
Reliability Modelling ◽  
Lifting System

Mechanical systems and their components usually have multiple failure modes and different performance states. Most existing system reliability modelling theories are developed on the basis of binary logic, which lack sufficient ability to describe the above phenomena. In this article, dynamic Bayesian network theory is employed to evaluate the multi-state reliability of a hydraulic lifting system. First, failure mode and effect analysis and structural analysis and design technique are comprehensively applied to analyse the functionalities and failure modes of the components. Afterwards, the time factor is integrated into the model by considering the state transition of the components. In this way, the multi-state reliability model of the system is established by dynamic Bayesian network. The reliability assessment and diagnostic analysis are performed by taking advantage of the dynamic Bayesian network’s bi-directional reasoning ability, and the results are in good agreement with actual situation. It shows that the proposed approach is effective and convenient for multi-state reliability modelling and analysis for mechanical systems.

Time and Spatial Modeling of Wear Mechanisms in Nuclear Steam Turbine Blades Using Dynamic Bayesian Networks

2018 ◽  
Author(s):  
David A. Quintanar-Gago ◽  
Pamela F. Nelson ◽  
Ángeles Díaz-Sánchez
Keyword(s):  
Bayesian Network ◽  
Failure Mode ◽  
Wear Mechanisms ◽  
Failure Modes ◽  
Turbine Blades ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Time Dependent ◽  
Prototype Model ◽  
Quantitative Methodology

This paper describes a quantitative methodology to estimate the probability of blade failure modes resulting from typical wear mechanisms in nuclear turbines, which can be used to optimize maintenance. The approach used to model time and spatial dependence of wear mechanisms that affect blades involves the coupling of a Static Bayesian Network to a Dynamic Bayesian Network. This prototype model has been designed to use conditional and time dependent Weibull-like failure rates that can be computed from reliability data bases (failure times and modes, associated causes, row and blade part that failed) to quantify Markov matrixes contained within dynamic nodes. The model can be used to make inferences such as the most probable causes of failure in a row and blade part, and visualize the probability as a function of time. It can be also used to determine the riskier location given evidence such as failure mode or the wear mechanisms involved. Also, maintenance tasks acting over time dependent failure functions have been implemented to exemplify the effect of perfect and three kinds of imperfect actions and how they affect the mechanisms and failure mode evolution, given the conditional dependences among them.

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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