Monitoring of Lubrication Regime in Deep Groove Ball Bearings Using Acoustic Emission Sensors

Industrial maintenance can be carried out in corrective, preventive, or predictive ways, with the latter relying on sensor-based equipment monitoring. These sensors track parameters such as vibration, noise, and temperature, preventing unexpected failures and downtime. The adoption of sensors for predictive maintenance has been growing in the industry, helping to avoid unplanned downtime and optimize asset lifespan. Among these, acoustic emission sensors excel in detecting energy waves generated by internal material phenomena, such as cracks or wear, which are influenced by lubrication conditions. Understanding the lubrication regime in which a bearing operates is crucial for its lifespan, as inadequate lubrication can accelerate wear and lead to premature failure. In this study, the performance of these sensors was compared with traditional piezoelectric accelerometers in analyzing the lubrication regime of ball bearings in laboratory tests using a SpectraQuest rotary machine fault simulator bench, varying two operational parameters: speed and radial load. Results showed that acoustic emission sensors demonstrated higher sensitivity in detecting lubrication regime variations through different signal processing features, namely: kurtosis, clearance factor, and impulse factor. Meanwhile, accelerometers didn't show sensitivity to varying the lubrication regime through radial load.