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