Use of Fourier transform for defining diagnostic parameters of car wheel

Augustynowicz, A.; Prażnowski, Krzysztof

doi:10.1109/sce.2011.6092126

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…There have been several other instances of wheel imbalance detection in instrumented vehicles [10]. A demonstrated in-car system for monitoring tire balance relies on accelerometers to identify radial and lateral acceleration of the tire and compare amplitudes to a known good baseline at characteristic frequencies, in effect examining differences in the amplitude of the Fourier component of radial acceleration [11] [12]. A variant on this setup uses an accelerometer on each wheel, to identify vibration and even calculate tire pressure, but cost and complexity of this system is significantly greater [13].…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Based Wheel Imbalance Detection

Siegel

Bhattacharyya

Deshpande

2015

Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and I

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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.

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Section: Introductionmentioning

confidence: 99%