Diagnostics and prognostics of a single surface defect on roller bearings

Bearing condition monitoring has been the focus of a wide range of studies over the past years. Current monitoring techniques that focus on the identi fication of faults present in a bearing have various limitations. Typically they are applicable only under well-defined, specific and precalibrated op erating conditions, thereby preventing continuous monitoring of a system op erating in a variant environment. They are often limited in damage severity estimation and prognostic capability. This, in turn, prevents the developm ent of optimal maintenance scheduling in favour of overall system safety an d productivity. Research presented in this paper has yielded results that h ave extended bearing diagnostics and prognostics to address these limitatio ns and to achieve optimal machinery maintenance scheduling. This paper disc usses the current research status on the development of a new signal proces sing method with noise cancellation capability to provide early defect dete ction, the establishment of a diagnostic model to estimate bearing defect s everity under variant conditions and the formulation of an adaptively tuned defect propagation model to track the time-variant nature of defect growth for the forecasting of bearing remaining utility.