Abstract
Automotive wheel misalignment is the most significant cause of excessive wear on tires, which will severely affect the stability and safety of vehicle handling, and cause serious consequences for human health and the environment. In this study, an energy-efficient onboard wheel alignment wireless monitoring system (WAWMS) is developed to detect wheel misalignment in real time. To minimise power consumption, a dual wake-up strategy is proposed to wake the microcontroller by a real-time clock (RTC) and an accelerometer. Furthermore, an online self-calibration method of inertial measurement unit (IMU) sampling frequency is investigated to improve measurement accuracy. Eventually, real-world wheel misalignment tests were performed with the WAWMS. The error-correcting output codes based support vector machines (ECOC-SVM) method successfully classifies different wheel alignment conditions with an average accuracy of 93.2% using nine principal components (PCs) of 3-axis acceleration spectrum matrixes. It validates the effectiveness of the designed WAWMS on automotive wheel alignment monitoring.
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