Bearing race fault detection using an optomechanical micro-resonator

Abstract

Bearing fault detection plays a crucial role in ensuring machinery reliability and safety. However, the existing bearing-fault-detection sensors are commonly too large to be embedded in narrow areas of bearings and too vulnerable to work in complex environment. Here, we demonstrate an approach to distinguish the presence of race faults in bearings and their types by using an optomechanical micro-resonator. The principle of the amplitude-frequency modulation model mixing fault frequency with mechanical frequency is raised to explain the asymmetrical sideband phenomena detected by the optical microtoroidal sensor. Kurtosis estimation used in this work can distinguish normal and faulty bearings in the time domain with the maximum accuracy rate of 91.72% exceeding the industry standard rate of 90%, while the amplitude-frequency modulation of the fault signal and mechanical mode is introduced to identify the types of the bearing faults, including, e.g., outer race fault and inner race fault. The fault-detection methods have been applied to the bearing on a mimic unmanned aerial vehicle (UAV), and correctly confirmed the presence of fault and the type of outer or inner race fault. Our study gives new perspectives for precise measurements on early fault warning of bearings, and may find applications in other fields such as vibration sensing.