Wheel Bearing Fault Detection for Automobiles using Wheel Speed Sensor

Wheel bearing fault diagnosis and monitoring for automobiles are getting more attention as the market shifts towards electric vehicles (EV) autonomous (AV). Electric drivetrains expected to have longer lifespan than reliable life of low-cost wheel designs. The probability having a failed in an EV thus increases. In AV setting, most rides taken by people who unfamiliar vehicle, removing human viable sensor detect defects. Fleet managers will be required pay high price inspect potential implementation detection system can help reduce these costs.
 our previous work, method injecting brinelling failure was developed. this paper, we propose algorithm using speed sensor. proposed detects faults identifying peaks spectrum at critical frequencies. To implement approach, leverage phase domain transform technique normalize against effect varying speed. A wavelet filtering is employed enhance signatures prior transforming signal into frequency domain. Post-processing techniques developed smooth high-variance spectra improve detectability faults. regression model then calculate health indicators. results show that achieves performance detecting