Dynamic Prognostic Prediction of Defect Propagation on Rolling Element Bearings

Abstract

Rolling element bearing failure is a major factor in the failure of rotating machinery. Current methods of bearing condition monitoring focus on determining any existing fault presence on a bearing as early as possible. Although a defect can be detected when it is well below the industry standard of a fatal size of 6.25 mm² (0.01 in²), the remaining life of a bearing (the time it takes to reach the final failure size) from the point where a defect 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 catastrophic consequences. Performing such an action, which usually occurs at inconvenient times, typically results in substantial time and economics losses. It is, therefore, important that the bearing's remaining life be more precisely forecasted, in a prognostic rather than diagnostic manner, so that maintenance can be optimally scheduled. Unfortunately, current bearing remaining life prediction methods have not been well developed due to the highly stochastic nature of the bearing fatigue process. This paper presents an adaptive bearing condition prognostic method that addresses the deficiency in current bearing remaining life prediction. In this paper, vibration and acoustic emission techniques are used to estimate defect severity by monitoring signals generated by a defective-bearing. A spall defect propagation process model is developed. Through the propagation model, in coupling with a defect diagnostic model, a recursive least square (RLS) algorithm is instituted to adoptively 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.