Dynamic prognostic prediction of defect propagation on rolling element bearings (c)

Rolling element bearing failure is a major factor in the failure of rotatin g machinery. Current methods of bearing condition monitoring focus on deter mining any existing fault presence on a bearing as early as possible. Altho ugh a defect can be detected when it is well below the industry standard of a fatal size of 6.25 mm(2) (0.01 in(2)) the remaining life of a bearing (t he time it takes to reach the final failure size) from the point where a de fect can be detected can vary substantially. As a fatal defect is detected, it is common to shut down machinery as soon as possible to avoid catastrop hic consequences. Performing such an action, which usually occurs at inconv enient times, typically results in substantial time and economics losses. I t is, therefore, important that the bearing's remaining life be more precis ely forecasted, in a prognostic rather than diagnostic manner; so that main tenance can be optimally scheduled. Unfortunately current bearing remaining life prediction methods have not been well developed due to the highly sto chastic nature of the bearing fatigue process. This paper presents an adaptive bearing condition prognostic method that ad dresses the deficiency in current bearing remaining life prediction. In thi s paper; vibration and acoustic emission techniques are used to estimate de fect severity by monitoring signals generated by a defective-bearing. A spa ll defect propagation process model is developed. Through the propagation m odel, in coupling with a defect diagnostic model, a recursive least square (RLS) algorithm is instituted to adaptively predict the defect growth rate. Results from numerical simulations and experimental bearing life tests are presented to verify the effectiveness of the adaptive prognostic scheme.