A Kalman Filter-Based Ensemble Approach With Application to Turbine Creep Prognostics

Piero Baraldi (1) , Francesca Mangili (1) , Enrico Zio (2)

1 Dipartimento di Energia [Milano]
2 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec

The safety of nuclear power plants can be enhanced, and the costs of operation and maintenance reduced, by means of prognostic and health management systems which enable detecting, diagnosing, predicting, and proactively managing the equipment degradation toward failure. We propose a prognostic method which predicts the Remaining Useful Life (RUL) of a degrading system by means of an ensemble of empirical models. The RUL predictions of the individual models are aggregated through a Kalman Filter (KF)-based algorithm. The method is applied to the prediction of the RUL of turbine blades affected by a developing creep.

Keywords: Creep; Kalman filter; ensemble; prognostics and health management

Domains: Engineering Sciences [physics]; Other

Piero Baraldi, Francesca Mangili, Enrico Zio. A Kalman Filter-Based Ensemble Approach With Application to Turbine Creep Prognostics. IEEE Transactions on Reliability, 2012, 61 (4), pp.966 - 977. ⟨10.1109/TR.2012.2221037⟩. ⟨hal-00777665⟩