Enhanced Fault Diagnosis of Bearings in Gearbox Based on Lucy-Richardson Deconvolution and Discussion

Abstract

Bearings are one of the most important components in rotating machineries. Their failures will lead to great production loss and increase the maintenance cost. So, condition monitoring work of bearings can save and avoid the potential loss caused by bearing fault. Lucy-Richardson deconvolution (LRD) algorithm, as an image processing technique, started to be used in bearing fault diagnosis. However, only data of bearings working in electric motor are used to validate the method. In engineering cases, most bearings are working in gearbox. Therefore, the bearing fault signals are very weak compared to the gear vibration signal. It is usually difficult to detect the bearing fault in this case. LRD algorithm is used to enhance the bearing fault diagnosis and some characteristics in this case are discussed. Experiment data analysis demonstrates that LRD can enhance the periodic impulse signal effectively. Otherwise, if the desired fault signal is weak compared to non-desired signal, then, the desired fault signal will be continued weaken by LRD which is not benefit to bearing’s incipient fault detection.