The Study of Trends in AI Applications for Vehicle Maintenance Through Keyword Co-occurrence Network Analysis
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Published
Oct 17, 2023
Wei Li
Guoyan Li
Sagar Kamarthi
Abstract
The increasing complexity of a vehicle's digital architecture has created new opportunities to revolutionize the maintenance paradigm. The Artificial Intelligence (AI) assisted maintenance system is a promising solution to enhance efficiency and reduce costs. This review paper studies the research trends in AI-assisted vehicle maintenance via keyword co-occurrence network (KCN) analysis. The KCN methodology is applied to systematically analyze the keywords extracted from 3153 peer-reviewed papers published between 2011 and 2022. The network metrics and trend analysis uncovered important knowledge components and structure of the research field covering AI applications for vehicle maintenance. The emerging and declining research trends in AI models and vehicle maintenance application scenarios were identified through trend visualizations. In summary, this review paper provides a comprehensive high-level overview of AI-assisted vehicle maintenance. It serves as a valuable resource for researchers and practitioners in the automotive industry. This paper also highlights potential research opportunities, limitations, and challenges related to AI-assisted vehicle maintenance.
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Keywords
Vehicle Maintenance, Artificial Intelligence, Prognostics and Health Management, Keyword Co-occurrence Network
References
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Felke, T., Holland, S., & Raviram, S. (2017). Integration of Component Design Data for Automotive Turbocharger with Vehicle Fault Model Using JA6268 Methodology. SAE International Journal of Passenger Cars - Electronic and Electrical Systems, 10(2), 380–389. https://doi.org/10.4271/2017-01-1623
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Technical Papers