In this paper, the development of a new prognostic estimation technique for on-line gear health management system is described and demonstrated with real spiral bevel gear run-to-failure test data. Unlike conventional particle filter-based prognostic estimation methods, the prognostic technique presented in this paper is a hybrid of the unscented Kalman filter and particle filter. It is designed to improve the processing efficiency whilst the state estimation accuracy is maintained. The unscented Kalman filter is utilized to obtain the “best estimate” of the states of a degrading nonlinear component and the particle filter l-step ahead prediction technique is employed to obtain the remaining useful life of the component. In addition, data mining techniques are applied to efficiently define the system dynamics model, observation model, and predicted measurement information for the prognostic estimator. At last, the feasibility of the presented prognostic estimator is demonstrated with satisfactory results using the actual oil debris mass and health index data obtained from a spiral bevel gear test rig.
