On the use of particle filters for prognostics in industrial applications

Hai Canh Vu (1) , Phuc Do Van (1) , Mayank Shekhar Jha (1) , Didier Theilliol (1) , Flavien Peysson

1 CRAN - Centre de Recherche en Automatique de Nancy

Prognostics is an engineering discipline aiming at predicting the Remaining Useful Life (RUL) of an industrial system or item. Accuracy and confident prediction of the RUL are very meaningful and important for anticipating failure, controlling system operational efficiency as well as optimizing maintenance operations. Given the important role of the prognostics or RUL prediction, a number of prognostics approaches has been proposed and successfully applied in various industry. Among these approaches, particle filters (PF) are more and more studied and employed thank to their powerful performance and their flexibility in predicting the RUL of systems non-linear and non-Gaussian. However, the prediction performance strongly depends on the application contexts and the type of particle filter utilized. The choice of particle filters is therefore a critical step in real industrial applications. The paper focuses on a comparison of the three different PF techniques (Sampling importance resampling, Auxiliary particle filter, and Regularized particle filter) to support the critical step. The performance of the three PF techniques is compared by considering different degradation models, noises level. In addition, the computing time is also analyzed through different numerical examples.

Mots clés: Failure Prediction; Prognostic; Particular filter

Domaines: Automatique / Robotique

Hai Canh Vu, Phuc Do Van, Mayank Shekhar Jha, Didier Theilliol, Flavien Peysson. On the use of particle filters for prognostics in industrial applications. The Fourth European Conference of the PHM Society, PHME18, Jul 2018, Utrecht, Netherlands. ⟨hal-01861221⟩