Grease-lubricated triboelectric instantaneous angular speed sensor integrated with signal processing circuit for bearing fault diagnosis

The triboelectric sensor can be integrated with the rolling bearing to diagnose bearing faults by monitoring the periodic fluctuation of instantaneous angular speed, yet continuous dry friction between materials will reduce its signal-to-noise ratio (SNR). Moreover, the signal processing modules used by previous triboelectric sensors need additional wires to transmit the signal, and thus affect the reliability of the system. In this manuscript, a wireless grease-lubricated triboelectric instantaneous angular speed sensor (GL-TEIASS) integrated with the signal processing circuit for bearing fault diagnosis is proposed based on rotary freestanding triboelectric nanogenerators (TENGs). First, electrical insulating grease is presented as an ideal candidate lubricant used in the GL-TEIASS. Next, by establishing the charge density-dependent electromechanical model of grease-lubricated TENG, optimal structural parameters for maximum signal-to-noise ratio and angular resolution as well as minimum friction torque of the GL-TEIASS can be obtained. Moreover, the kurtosis-guided local polynomial fitting method is employed to extract the signal fluctuation caused by bearing faults. The results show that, compared to dry friction, the mass loss of the polymer under grease lubrication decreases by 91.7 % and the electrical output increases by 4.0 times. The durability of the GL-TEIASS in fault diagnosis is improved by at least 2.8 times compared with that of the sensor operating under dry friction. The error between the angular speed measured by the GL-TEIASS and a commercial sensor is less than 0.731 %, and the SNR of the output signal from in-situ monitoring by the GL-TEIASS can reach 39.1 dB, which is 4.9 times higher than that of vibration signal commonly used in fault diagnosis. The designed GL-TEIASS has good application prospects in self-sensing and self-diagnosis of operation state for smart bearings.