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Smartphone-Based Wheel Imbalance Detection

[+] Author Affiliations
Joshua E. Siegel, Rahul Bhattacharyya, Sanjay Sarma

Massachusetts Institute of Technology, Cambridge, MA

Ajay Deshpande

IBM Research, Yorktown Heights, NY

Paper No. DSCC2015-9716, pp. V002T19A002; 10 pages
doi:10.1115/DSCC2015-9716
From:
  • ASME 2015 Dynamic Systems and Control Conference
  • Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications
  • Columbus, Ohio, USA, October 28–30, 2015
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5725-0
  • Copyright © 2015 by ASME

abstract

Onboard sensors in smartphones present new opportunities for vehicular sensing. In this paper, we explore a novel application of fault detection in wheels, tires and related suspension components in vehicles. We present a technique for in-situ wheel imbalance detection using accelerometer data obtained from a smartphone mounted on the dashboard of a vehicle having balanced and imbalanced wheel conditions. The lack of observable distinguishing features in a Fourier Transform (FT) of the accelerometer data necessitates the use of supervised machine learning techniques for imbalance detection. We demonstrate that a classification tree model built using Fourier feature data achieves 79% classification accuracy on test data. We further demonstrate that a Principal Component Analysis (PCA) transformation of the Fourier features helps uncover a unique observable excitation frequency for imbalance detection. We show that a classification tree model trained on randomized PCA features achieves greater than 90% accuracy on test data. Results demonstrate that the presence or absence of wheel imbalance can be accurately detected on at least two vehicles of different make and model. Sensitivity of the technique to different road and traffic conditions is examined. Future research directions are also discussed.

Copyright © 2015 by ASME
Topics: Wheels

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