Development of compact smart bearing and novel hybrid feature assessment for weak defect identification

In this study, a compact smart bearing with self-sensing and condition monitoring function is proposed. A traditional bearing embedded in a piezoelectric transducer ring with a segmented electrode design is prototyped and tested under different working conditions. Its compact design ensures compatibility for integration with different systems. The segmented electrodes enable possible multidirectional data acquisition and piezoelectric energy generation, suggesting potential integration with Internet of Things (IoT) platforms. Utilizing the high sensitivity, fast response and high signal resolution of the piezoelectric material in conjunction with a novel design that enables close contact between the piezoelectric transducer and the bearing, the smart bearing demonstrates effective performance in detecting weak bearing defects signals. A novel feature characterising method is proposed, and a hybridised feature selection method is employed for reducing the dimension of feature subsets and ensuring defect identification accuracy. A classification model for the identification of defects is developed based on a Long Short-Term Memory (LSTM) network. The performance of the smart bearing and the method for identifying the defects are evaluated through experiments to demonstrate the potential for practical applications. A preliminary experiment for energy harvesting using the smart bearing has been conducted, and it proves the potential to sustain power.