A miniature and intelligent Low-Power in situ wireless monitoring system for automotive wheel alignment

Xiaoli Tang; Yu Shi; Boyue Chen; Mark Longden; Rabiya Farooq; Harry Lees; Yu Jia

doi:10.1016/j.measurement.2023.112578

A miniature and intelligent Low-Power in situ wireless monitoring system for automotive wheel alignment

Xiaoli Tang
, Yu Shi
, Boyue Chen
, Mark Longden
, Rabiya Farooq
, Harry Lees
, Yu Jia^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

6 Citations (SciVal)

8 Downloads (Pure)

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.

Original language	English
Article number	112578
Number of pages	13
Journal	Measurement: Journal of the International Measurement Confederation
Volume	211
Early online date	18 Feb 2023
DOIs	https://doi.org/10.1016/j.measurement.2023.112578
Publication status	Published - 1 Apr 2023

Bibliographical note

Funding

The authors thank the Centre for Efficiency and Performance Engineering (CEPE) at the University of Huddersfield for supporting the shaker test in this research. This project was funded under contract within the Clean Air Program by Innovate UK SBRI 971703 as part of the AutoAlign project led by RL Automotive with subcontractors, Aston University and the University of Chester.

Funders	Funder number
Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK. Electronic address: [email protected].
Innovate UK SBRI	971703
University of Huddersfield
University of Chester
Aston University

Keywords

Condition monitoring
Dual wake-up strategy
ECOC-SVM
Low power consumption
Wheel alignment
Wheel alignment wireless monitoring system

Access to Document

10.1016/j.measurement.2023.112578

A miniature and intelligent Low-Power in situ wireless monitoring system for automotive wheel alignment
© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Final published version, 4.94 MBLicence: CC BY 4.0

Cite this

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title = "A miniature and intelligent Low-Power in situ wireless monitoring system for automotive wheel alignment",

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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AU - Tang, Xiaoli

AU - Shi, Yu

AU - Chen, Boyue

AU - Longden, Mark

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AU - Lees, Harry

AU - Jia, Yu

PY - 2023/4/1

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N2 - 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.

AB - 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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A miniature and intelligent Low-Power in situ wireless monitoring system for automotive wheel alignment

Abstract

Bibliographical note

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Keywords

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