Title: Vehicle abnormal jitter detection based on multi-task convolutional neural network

Authors: Jun Liu; Xiaoyuan Luo

Addresses: Faculty of Science, Heihe University, Heihe Heilongjiang, 164300, China ' Faculty of Science, Heihe University, Heihe Heilongjiang, 164300, China

Abstract: In order to overcome the problems of poor convergence, long detection time and high misjudgement rate existing in traditional methods, this paper proposes a vehicle abnormal jitter detection method based on multi-task convolutional neural network. The causes of abnormal vehicle jitter were analysed, and the equivalent taper of vehicles under different mileage was calculated to obtain the abnormal vehicle jitter frequency, and the time-frequency characteristics of vehicle vibration were obtained. The multi-task convolutional neural network is used to establish the calculation model of wheel matching geometric relationship under the condition of dithering, and the dithering degree of the vehicle body at different moments is obtained by sections, so as to realise the detection of abnormal vehicle dithering. The convergence coefficient of the method in this paper is higher than 0.9, the detection time of abnormal jitter is always below 1.2s, and the lowest misjudgement rate is only 0.6%.

Keywords: multi-task convolutional neural network; dithering detection; integral method; dithering frequency; equivalent taper.

DOI: 10.1504/IJVD.2022.128018

International Journal of Vehicle Design, 2022 Vol.89 No.1/2, pp.114 - 126

Received: 24 May 2021
Accepted: 25 Aug 2021
Published online: 04 Jan 2023 *

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