Sequential Hybrid Method for Full Lifetime Remaining Useful Life Prediction of Bearings in Rotating Machinery

Abstract

Optimal scheduling of the maintenance of bearings in rotating machinery requires accurate remaining useful life (RUL) prediction during the entire lifetime of the bearing. For that reason, this paper proposes a sequential hybrid method that combines the strengths of statistical and data-driven approaches. A statistical model-based approach is preferred before a bearing fault is detected, and a data-driven approach once a bearing fault is detected from the vibration measurements. The method is tested and evaluated on an extensive dataset of accelerated lifetime tests of deep groove ball bearings. It is shown that the method, with a limited amount of training data, delivers accurate RUL predictions during both the healthy stage of the bearing lifetime, as well as during the final stages of increasing degradation under both constant and varying speed conditions.