Self-powered fault diagnosis of rolling bearings based on triboelectric effect

In this study, self-powered fault diagnosis of rolling bearing (SP-FDRB) is conducted based on the triboelectric effect. Flexible interdigital electrodes are glued to the outer ring of a rolling bearing to form a rolling-type free standing mode triboelectric nanogenerator (RF-TENG). The RF-TENG has adequate service life because it is designed to avoid direct contact between the flexible electrode and the rolling element and maintain the structural integrity of the bearing. A prototype of the RF-TENG structure is fabricated and the power generation mechanism of the RF-TENG is analyzed and verified by electrostatic field simulation. The electrical output test of the RF-TENG is conducted. The variations in the output current amplitude and frequency with change in the speed of rotation are analyzed, and the maximum output power of the RF-TENG corresponding to the optimal load is obtained accordingly. The reliability and bearing capacity of the RF-TENG are illustrated through long-term continuous operation and various bearing load tests. Based on the output current signal model, the fault characteristic frequency of the RFTENG output current is obtained when the outer ring, inner ring, and rolling ball have localized faults. SP-FDRB is performed based on the RF-TENG output current signal by utilizing several machine learning algorithms. The results show that the classification accuracy can exceed 92%, which is equivalent to the accuracy based on the vibration signal. The proposed RF-TENG based SP-FDRB is feasible and has excellent application prospect.

Automated summary

This study conducted self-powered fault diagnosis of rolling bearings based on the triboelectric effect. The RF-TENG structure was designed and tested, achieving a classification accuracy exceeding 92%. The proposed RF-TENG based SP-FDRB is feasible and has excellent application prospect.