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A Miniature and Intelligent Low-Power in situ Wireless Monitoring System for Automotive Wheel Alignment
Tang, Xiaoli Chen, Boyue Longden, Mark Farooq, Rabiya Lees, Harry Yu, Jia Shi, Yu
Tang, Xiaoli
Chen, Boyue
Longden, Mark
Farooq, Rabiya
Lees, Harry
Yu, Jia
Shi, Yu
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Other Contributors
EPub Date
Publication Date
2023-02-10
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Article - AAM
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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.
Citation
Tang, X., Shi, Y., Chen, B., Longden, M., Farooq, R., Lees, H., & Jia, Y. (2023). A miniature and intelligent low-power in situ wireless monitoring system for automotive wheel alignment. Measurement, 211, 112578. https://doi.org/10.1016/j.measurement.2023.112578
Publisher
Elsevier
Journal
Measurement
Research Unit
DOI
10.1016/j.measurement.2023.112578
PubMed ID
PubMed Central ID
Type
Article
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Description
Series/Report no.
ISSN
0263-2241