A dynamic Bayesian network approach for prognosis computations on discrete states systems
Résumé
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 howDynamic Bayesian Networks (DBN) can be relevant to represent multicomponent complex systems and carry out reliability studies. The Diagnosis & maintenance group from IFSTTAR developed a model (VirMaLaB: Virtual Maintenance Laboratory) based on DBN 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 DBN. Adiscrete state space system is considered, periodically observable through a diagnosis process Such systems are common in railway or road infrastructures fields. This paper presents a prognosis algorithm whose purpose is to compute the Remaining useful life (RUL) of the system and update this estimation each time a new diagnosis is available. Then, a representation of this algorithm is given as a DBN in order to be next integrated into the VirMaLaB model to include the set of predictive maintenance policies. Inference computation questions on the considered DBN will be discussed. Finally, an application on simulated data will be presented.
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