Prognostics and health management allows us to predict the remaining useful life (RUL) of machinery, which is important in reducing maintenance costs and downtime, and even preventing casualties. Bearing faults account for a large proportion of machine faults. To predict the RUL of bearings, health indicators that represent the degeneration state are extracted based on the Hilbert–Huang transform and selected according to Spearman's coefficient. A model-based particle filter method is then used to track the degradation state. The unknown parameters in the nonlinear system are updated by a new method of logarithmic linear recursive least squares. A recursive maximum likelihood estimation algorithm is introduced to learn the noise in the system, and an innovative parameter fusion technique based on normalized partial derivative weights is used. Finally, the RUL of the bearings is predicted. The proposed method is validated using data from the test platform PRONOSTIA.
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