Remaining useful life prediction of rolling element bearings using degradation feature based on amplitude decrease at specific frequencies

This research presents a new method of degradation feature extraction to predict remaining useful life, the remaining time to the maintenance, of rolling element bearings. Since bearing fault is the foremost cause of failure in rotating machinery, there are many studies for evaluating bearings’ health status to prevent a catastrophic failure. Most of these studies are based on health monitoring data, such as vibration signals that are indirectly related to bearing fault, from which degradation feature can be extracted. It is, however, challenging to extract a degradation feature that can be applied to all rolling elements. This study focuses on the amplitude decrease at specific frequencies, from which a robust degradation feature is extracted by employing the information entropy. Some important attributes are found from the degradation feature, which is used to predict the remaining useful life of bearings. This method is demonstrated using the real test data provided by FEMTO-ST Institute. The results show that bearings can be used up to 87% of their whole life and 59%–74% of life in average.