Fault detection and isolation of bearings in a drive reducer of a hot steel rolling mill

Defective bearings are a major concern in rotating machinery. In this work we propose a two-step scheme, relying on two complementary data-driven techniques, for fault detection and isolation for a drive reducer in a hot steel rolling mill. A preliminary fault detection phase is based on a computationally lightweight time-domain multivariate statistical technique. Secondly, a more computationally intensive frequency-domain analysis method is used to confirm the fault detection and provide information on its frequency characteristics. Automatic procedures are sketched for the application of both techniques. Bearing defect models are employed to test their fault detection and isolation capabilities.