Prognostic and Health Management (PHM) represents an active field of research and a major scientific challenge in many domains. It usually focuses on the failure time or the Remaining Useful Life (RUL) prediction of a system.
This paper presents a generic framework, based on a discrete Bayesian Network (BN), particularly tailored for decision fusion of heterogeneous prognostic methods. The BN parameters are computed according to the fixed prognostic objectives.
The effectiveness of the proposed decision fusion based methodology for the prognostic is demonstrated through the RULs estimation of turbofan engines. The application highlights the ability of the approach to estimate RULs which overpasses the performance of most other published results in the literature
