Modeling dynamic reliability using dynamic Bayesian networks

This paper considers the problem of modeling and analyzing the reliability of a system or a component (system) where the state of the system and the state of process variables influences each other in addition to an exogenous perturbation influence: this is the dynamic reliability. We consider discrete time case, that is the state of the system as well as the state of process variables are observed or measured at discrete time instants. A mathematical tool that shows interesting properties for modeling and analyzing this problem is the so called Dynamic Bayesian Networks (DBN) that permit graphical representation of stochastic processes. Furthermore their learning and inference capabilities can be exploited to take into account experimental data or expert’s knowledge. We will show that a complex interaction between system and process on one hand and between system, process and exogenous perturbation on the other hand can simply be represented graphically by a dynamic Bayesian network. With their extended tool, known as influence diagrams (ID) that integrate actions or decisions possibilities, one can analyze and optimize a maintenance policy and/or make reactive decision during an accident by simulating different scenarios of its evolution for instance

Exploiting local and repeated structure in dynamic Bayesian networks

status: publishe

DYNAMIC PROBABILITY FAILURE USING BAYESIAN NETWORK FOR HYDROGEN INFRASTRUCTURE MODELING

To produce large scale hydrogen production, it requires adequate and efficient risk control. For decades, fault tree analysis was the most widely used tool for risk assessment for industrial sector generally and hydrogen infrastructure particularly in terms of risk and consequences associated to it. The limitation to this tool is it tends to be static and do not develop over time which can give unreliable estimation of risk. The purpose of this project is to study the suitability and efficiency of dynamic Bayesian Networks in terms of projecting the risk probability failure that develop over time for hydrogen infrastructure as the alternative of the fault tree analysis. In this study, only the risk probability failure is covered without further exploration on the consequences of the risk. The process involved by the conversion of fault tree to Bayesian Networks model by using appropriate framework. Then, the conditional probability table is assigned to each node where the numbers of CPT depend on the numbers of relationship between nodes. Finally the temporal reasoning is done to show the time-invariant between each node and the beliefs is updated to get the results. The ways of inference use for this study are filtering and smoothing. The results show that generally, the OR gates contribute to higher risk probability compare to AND gates. Besides that, the probability for hydrogen activities increase from year to year with the assumption the accident did not happen the previous year. In addition, the instantaneous release incident is relatively low and unlikely to happen compare to the continuous release

Probabilistic robotic logic programming with hybrid Boolean and Bayesian inference

Bayesian inference provides a probabilistic reasoning process for drawing conclusions based on imprecise and uncertain data that has been successful in many applications within robotics and information processing, but is most often considered in terms of data analysis rather than synthesis of behaviours. This paper presents the use of Bayesian inference as a means by which to perform Boolean operations in a logic programme while incorporating and propagating uncertainty information through logic operations by inference. Boolean logic operations are implemented in a Bayesian network of Bernoulli random variables with tensor-based discrete distributions to enable probabilistic hybrid logic programming of a robot. This enables Bayesian inference operations to coexist with Boolean logic in a unified system while retaining the ability to capture uncertainty by means of discrete probability distributions. Using a discrete Bayesian network with both Boolean and Bayesian elements, the proposed methodology is applied to navigate a mobile robot using hybrid Bayesian and Boolean operations to illustrate how this new approach improves robotic performance by inclusion of uncertainty without increasing the number of logic elements required. As any logical system could be programmed in this manner to integrate uncertainty into decision-making, this methodology can benefit a wide range of applications that use discrete or probabilistic logic