Detecting high fuel consumption in HDVs with ensemble of anomaly detection models

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Turan, Berkay Barış and Genç, Emre and Akçığ, İnci Nil and Göztepe, Neslihan and Mumcuoğlu, Mehmet Emin and Ünel, Mustafa (2024) Detecting high fuel consumption in HDVs with ensemble of anomaly detection models. In: 22nd IEEE International Conference on Industrial Informatics (INDIN), Beijing, China

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Abstract

This paper presents a machine learning (ML)-based system designed to detect high fuel consumption in heavy-duty vehicles (HDVs) using operational data. The system addresses environmental and efficiency challenges in the transportation industry by precisely monitoring fuel consumption to curb CO2 emissions. An ensemble learning method that integrates unsupervised anomaly detection techniques, including Isolation Forest, Autoencoder, and k-NN Regressor models, is proposed. The anomaly detection results from these models are combined using a weighted majority voting (WMV) approach. This method was tested on a dataset comprising 459 driving records and 14 signals collected from 187 HDVs. Additionally, the Local Outlier Factor (LOF) model was employed to validate the ensemble learning method and identify the root causes of the anomalies. This work contributes to the field of transportation efficiency by offering a novel approach to analyzing fuel consumption in HDVs, thereby paving the way for future advancements in sustainable transportation practices.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: Machine Learning, Fuel Consumption, Heavy-Duty Vehicles, Anomaly Detection, Environmental Sustainability
Subjects: T Technology > TJ Mechanical engineering and machinery > TJ163.12 Mechatronics
T Technology > TJ Mechanical engineering and machinery > TJ170-179 Mechanics applied to machinery. Dynamics
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences > Academic programs > Industrial Engineering
Faculty of Engineering and Natural Sciences > Academic programs > Mechatronics
Faculty of Engineering and Natural Sciences
Depositing User: Mustafa Ünel
Date Deposited: 29 Sep 2024 13:26
Last Modified: 29 Sep 2024 13:26
URI: https://research.sabanciuniv.edu/id/eprint/50284

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