Smartphone-Based Wheel Imbalance Detection

• Main Authors: Siegel, Joshua E (Contributor), Bhattacharyya, Rahul (Contributor), Sarma, Sanjay E (Contributor), Deshpande, Ajay A. (Contributor)
• Other Authors: Massachusetts Institute of Technology. Department of Civil and Environmental Engineering (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor), Subirana, Brian (Contributor)
• Format: Article
• Language: English
• Published: American Society of Mechanical Engineers, 2018-08-20T17:40:46Z.
• Subjects: Article
• Online Access: Get fulltext

Onboard sensors in smartphones present new opportunities for vehicular sensing. In this paper, we explore a novel appli- cation 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 bal- anced 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) trans- formation of the Fourier features helps uncover a unique observ- able 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 ac- curately 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.