The Study of Trends in AI Applications for Vehicle Maintenance Through Keyword Co-occurrence Network Analysis

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.

Multi-Output Deep Learning Model for Fault Diagnosis Based on Time-Series Data

“…In recent years, deep learning methods, which are also datadriven methods, come as an attractive direction to address the problem of fault diagnosis (Li, Li, & Kamarthi, 2023). Due to its enormous representing power and automated feature learning capability in solving complex problems, many researchers have adopted it for solving this problem.…”