An Integrated Approach for Gear Health Prognostics

In this paper, an integrated approach for gear health prognostics using particle filters is presented. The presented method effectively addresses the issues in applying particle filters to gear health prognostics by integrating several new components into a particle filter: (1) data mining based techniques to effectively define the degradation state transition and measurement functions using a one-dimensional health index obtained by whitening transform; (2) an unbiased l-step ahead RUL estimator updated with measurement errors. The feasibility of the presented prognostics method is validated using data from a spiral bevel gear case study.