Tian, Daxin, Zhu, Yukai, Duan, Xuting, Hu, Junjie, Sheng, Zhengguo, Chen, Min, Wang, Jian and Wang, Yunpeng (2019) An effective fuel level data cleaning and repairing method for vehicle monitor platform. IEEE Transactions on Industrial Informatics, 15 (1). pp. 410-416. ISSN 1551-3203

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Abstract

With energy scarcity and environmental pollution becoming increasingly serious, the accurate estimation of fuel consumption of vehicles has been important in vehicle management and transportation planning towards a sustainable green transition. Fuel consumption is calculated by fuel level data collected from high precision fuel level sensors. However, in the vehicle monitor platform, there are many types of error in the data collection and transmission processes, such as the noise, interference, and collision errors are common in the high speed and dynamic vehicle environment. In this paper, an effective method for cleaning and repairing the fuel level data is proposed, which adopts the threshold to acquire abnormal fuel data, the time quantum to identify abnormal data, and linear interpolation based algorithm to correct data errors. Specifically, a modified Gaussian Mixture Model (GMM) based on the synchronous iteration method is proposed to acquire the thresholds, which uses the Particle Swarm Optimization (PSO) algorithm and the steepest descent algorithm to optimize the parameters of GMM. The experiment results based on the fuel level data of vehicles collected over one month prove the modified GMM is superior to GMM-EM on fuel level data, and the proposed method is effective for cleaning and repairing outliers of fuel level data.

Item Type:	Article
Schools and Departments:	School of Engineering and Informatics > Engineering and Design
Research Centres and Groups:	Communications Research Group
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication Including telegraphy, telephone, radio, radar, television
Depositing User:	Zhengguo Sheng
Date Deposited:	30 Oct 2018 12:59
Last Modified:	14 Jan 2019 16:17
URI:	http://srodev.sussex.ac.uk/id/eprint/79808

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