Self-powered sensor for online monitoring of eccentricity faults in rotating machinery and its application in spindle eccentricity monitoring of machine tools

With their unique structures and functionalities, rotating machineries are widely employed in various high-end industries. However, eccentric faults of rotors during continuous high-speed operation represent a critical factor in causing safety accidents. Therefore, real-time monitoring of rotor health is crucial for promptly identifying potential safety hazards and fault indications, enabling preventative maintenance, and enhancing safety performance. Nonetheless, traditional monitoring methods rely on sensors installed on rotating components, which are prone to interference in rotational environments, involve complex installation and maintenance procedures, and pose significant challenges to rotor health monitoring. In this study, a self-powered rotor eccentricity monitoring sensor (RE-TENGs) is designed and fabricated, integrating triboelectric nanogenerator technology. This sensor utilizes the rotor as the self-sensing entity, requiring no external power supply and featuring simplified installation, enabling real-time monitoring of rotor health during rotational machinery operation. A linear relationship between the RE-TENG's eccentric response and electrical characteristics was established through dynamic testing. Furthermore, the actual eccentricity orientation of the rotor was successfully determined by employing vector analysis and synthesis methods with multiple RE-TENGs coupled. To further validate the practicality and accuracy of RE-TENGs, a monitoring testbed for eccentric faults of the spindle rotor in a CNC machine tool was constructed, with the spindle rotor's eccentric fault simulated through misaligned installation. Experimental results demonstrate that RE-TENGs can effectively detect rotor faults induced by eccentric forces during the actual operation of machine tools, accurately determining the eccentric direction and displacement. With an actual angle recognition error of less than ±0.824° and an eccentric distance recognition accuracy exceeding 98%, the exceptional performance of RE-TENGs in monitoring and precise identification of eccentric faults in rotating machinery rotors is unequivocally verified. By integrating triboelectric nanogenerator (TENG) technology, this study realizes real-time monitoring and precise identification of eccentric faults in rotating machinery rotors. It provides a novel solution for health condition monitoring of rotating machinery and offers a reference for overcoming the cumbersome installation and complex wiring of traditional sensors in practical applications.