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



Published Oct 17, 2023
Wei Li Guoyan Li Sagar Kamarthi


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.

Abstract 367 | PDF Downloads 358



Vehicle Maintenance, Artificial Intelligence, Prognostics and Health Management, Keyword Co-occurrence Network

Abid, F. B., Sallem, M., & Braham, A. (2020). Robust Interpretable Deep Learning for Intelligent Fault Diagnosis of Induction Motors. IEEE Transactions on Instrumentation and Measurement, 69(6), 3506–3515.
AECC. (n.d.). Home. Automotive Edge Computing Consortium. Retrieved March 20, 2023, from
Agrawal, M., Eloot, K., Mancini, M., & Patel, A. (2020, July 29). Industry 4.0: Reimagining manufacturing operations after COVID-19 | McKinsey. McKinsey&Company.
Aguilar, D. L., Medina-Perez, M. A., Loyola-Gonzalez, O., Choo, K.-K. R., & Bucheli-Susarrey, E. (2023). Towards an Interpretable Autoencoder: A Decision-Tree-Based Autoencoder and its Application in Anomaly Detection. IEEE Transactions on Dependable and Secure Computing, 20(2), 1048–1059.
AllCarFix. (2022, May 14). How Many Sensors on a Car—List of 8 Important Car Sensors. AllCarFix.
Al-Zeyadi, M., Andreu-Perez, J., Hagras, H., Royce, C., Smith, D., Rzonsowski, P., & Malik, A. (2020). Deep Learning Towards Intelligent Vehicle Fault Diagnosis. 2020 International Joint Conference on Neural Networks (IJCNN), 1–7.
Arena, F., Collotta, M., Luca, L., Ruggieri, M., & Termine, F. G. (2022). Predictive Maintenance in the Automotive Sector: A Literature Review. Mathematical and Computational Applications, 27(1), Article 1.
Aslanpour, M. S., Gill, S. S., & Toosi, A. N. (2020). Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research. Internet of Things, 12, 100273.
Avatefipour, O., & Malik, H. (2018). State-of-the-Art Survey on In-Vehicle Network Communication (CAN-Bus) Security and Vulnerabilities (arXiv:1802.01725). arXiv.
Azure. (n.d.). Azure IoT Edge. Microsoft Azure. Retrieved March 20, 2023, from
Brito, L. C., Susto, G. A., Brito, J. N., & Duarte, M. A. V. (2022). An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery. Mechanical Systems and Signal Processing, 163, 108105.
Brusamarello, B., Cardozo da Silva, J. C., de Morais Sousa, K., & Guarneri, G. A. (2023). Bearing Fault Detection in Three-Phase Induction Motors Using Support Vector Machine and Fiber Bragg Grating. IEEE Sensors Journal, 23(5), 4413–4421.
Bühler, M. M., Jelinek, T., & Nübel, K. (2022). Training and Preparing Tomorrow’s Workforce for the Fourth Industrial Revolution. Education Sciences, 12(11), Article 11.
Carvalho, T. P., Soares, F. A. A. M. N., Vita, R., Francisco, R. da P., Basto, J. P., & Alcalá, S. G. S. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024.
Coffin, D., Downing, D., Horowitz, J., & LaRocca, G. (2022). The Roadblocks of the COVID-19 Pandemic in the U.S. Automotive Industry. SSRN Electronic Journal.
Coleman, C., Damodaran, S., Chandramouli, M., & Deuel, E. (2017, May 9). Making maintenance smarter. Deloitte Insights.
Costa, N., & Sánchez, L. (2022). Variational encoding approach for interpretable assessment of remaining useful life estimation. Reliability Engineering & System Safety, 222, 108353.
Cui, S., Shin, J., Woo, H., Hong, S., & Joe, I. (2020). State-of-Health Estimation of Lithium-Ion Batteries with Attention-Based Deep Learning. In R. Silhavy, P. Silhavy, & Z. Prokopova (Eds.), Software Engineering Perspectives in Intelligent Systems (pp. 322–331). Springer International Publishing.
Design and Run-Time Information Exchange for Health-Ready Components. (2023). SAE International.
Du, C., Zhang, S., Lin, Z., & Yu, F. (2019). Fault Identification of Vehicle Automatic Transmission based on Sparse Autoencoder and Support Vector Machine. IOP Conference Series: Materials Science and Engineering, 490(7), 072050.
Esperon-Miguez, M., John, P., & Jennions, I. K. (2013). A review of Integrated Vehicle Health Management tools for legacy platforms: Challenges and opportunities. Progress in Aerospace Sciences, 56, 19–34.
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.
Gherbi, E., Hanczar, B., Janodet, J.-C., & Klaudel, W. (2022). DAD: A Distributed Anomaly Detection framework for future In-vehicle network. 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 1–6.
Ghimire, R., Zhang, C., & Pattipati, K. R. (2018). A Rough Set-Theory-Based Fault-Diagnosis Method for an Electric Power-Steering System. IEEE/ASME Transactions on Mechatronics, 23(5), 2042–2053.
Goyal, D., Choudhary, A., Pabla, B. S., & Dhami, S. S. (2020). Support vector machines based non-contact fault diagnosis system for bearings. Journal of Intelligent Manufacturing, 31(5), 1275–1289.
Gültekin, Ö., Cinar, E., Özkan, K., & Yazıcı, A. (2022a). Multisensory data fusion-based deep learning approach for fault diagnosis of an industrial autonomous transfer vehicle. Expert Systems with Applications, 200, 117055.
Gültekin, Ö., Cinar, E., Özkan, K., & Yazıcı, A. (2022b). Real-Time Fault Detection and Condition Monitoring for Industrial Autonomous Transfer Vehicles Utilizing Edge Artificial Intelligence. Sensors, 22(9), Article 9.
Haque, M., Shaheed, M. N., & Choi, S. (2018). Deep Learning Based Micro-Grid Fault Detection and Classification in Future Smart Vehicle. 2018 IEEE Transportation Electrification Conference and Expo (ITEC), 1082–1107.
Hensley, R., Maurer, I., & Padhi, A. (2021, July 16). Automotive industry after COVID-19 | McKinsey. McKensey&Company.
Hu, W., Sun, Q., & Mechefske, C. K. (2013). Condition monitoring for the endurance test of automotive light assemblies. The International Journal of Advanced Manufacturing Technology, 66(5), 1087–1095.
Hu, Y., Miao, X., Si, Y., Pan, E., & Zio, E. (2022). Prognostics and health management: A review from the perspectives of design, development and decision. Reliability Engineering & System Safety, 217, 108063.
IBM. (2022, July 26). IBM Edge Application Manager. IBM.
IVHM Design Guidelines. (2019). SAE International.
Iyengar, A., & Portilla, I. (2022, October 24). Models Deployed at the Edge. IBM.
Jia, X., Duan, S., Lee, C., Radecki, P., & Lee, J. (2019). A Methodology for the Early Diagnosis of Vehicle Torque Converter Clutch Degradation. 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), 529–534.
Ke, Y., Zhou, R., Zhu, R., & Peng, W. (2021). State of Health Estimation of Lithium Ion Battery with Uncertainty Quantification Based on Bayesian Deep Learning. 2021 3rd International Conference on System Reliability and Safety Engineering (SRSE), 12–18.
Kim, K., Son, S., & Lee, B. (2020). Autonomous Vehicles Diagnosis Platform(AVDP) based on deep learning and loopback. 2020 International Conference on Information Networking (ICOIN), 687–689.
Kim, M. S., Yun, J. P., & Park, P. (2022). Deep Learning-Based Explainable Fault Diagnosis Model With an Individually Grouped 1-D Convolution for Three-Axis Vibration Signals. IEEE Transactions on Industrial Informatics, 18(12), 8807–8817.
Kim, S. W., Oh, K.-Y., & Lee, S. (2022). Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries. Applied Energy, 315, 119011.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), Article 7553.
Li, D., Liu, P., Zhang, Z., Zhang, L., Deng, J., Wang, Z., Dorrell, D. G., Li, W., & Sauer, D. U. (2022). Battery Thermal Runaway Fault Prognosis in Electric Vehicles Based on Abnormal Heat Generation and Deep Learning Algorithms. IEEE Transactions on Power Electronics, 37(7), 8513–8525.
Li, G., Yuan, C., Kamarthi, S., Moghaddam, M., & Jin, X. (2021). Data science skills and domain knowledge requirements in the manufacturing industry: A gap analysis. Journal of Manufacturing Systems, 60, 692–706.
Li, T., Sun, C., Li, S., Wang, Z., Chen, X., & Yan, R. (2022). Explainable Graph Wavelet Denoising Network for Intelligent Fault Diagnosis. IEEE Transactions on Neural Networks and Learning Systems, 1–14.
Li, T., Zhao, Z., Sun, C., Cheng, L., Chen, X., Yan, R., & Gao, R. X. (2022). WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(4), 2302–2312.
Li, Y., Maleki, M., Banitaan, S., & Chen, M. (2022). State of Health Indicator Modeling of Lithium-ion Batteries Using Machine Learning Techniques. 2022 IEEE International Conference on Electro Information Technology (eIT), 440–445.
Liu, H., Liu, Z., Jia, W., & Lin, X. (2021). Remaining Useful Life Prediction Using a Novel Feature-Attention-Based End-to-End Approach. IEEE Transactions on Industrial Informatics, 17(2), 1197–1207.
Lo, N. G., Flaus, J.-M., & Adrot, O. (2019). Review of Machine Learning Approaches In Fault Diagnosis applied to IoT Systems. 2019 International Conference on Control, Automation and Diagnosis (ICCAD), 1–6.
Long, J., Mou, J., Zhang, L., Zhang, S., & Li, C. (2021). Attitude data-based deep hybrid learning architecture for intelligent fault diagnosis of multi-joint industrial robots. Journal of Manufacturing Systems, 61, 736–745.
Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. 30). Curran Associates, Inc.
Mwangi, D., Trivedi, T., & Kothari, N. (2022). Open Switch Fault Detection in Electric Vehicle Drives Using Support Vector Machine. 2022 2nd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON), 1–6.
Nieto González, J. P. (2018). Vehicle fault detection and diagnosis combining an AANN and multiclass SVM. International Journal on Interactive Design and Manufacturing (IJIDeM), 12(1), 273–279.
Noble, W. S. (2006). What is a support vector machine? Nature Biotechnology, 24(12), Article 12.
NVIDIA. (n.d.). The EGX Platform. NVIDIA. Retrieved March 20, 2023, from
Onnela, J.-P., Saramäki, J., Kertész, J., & Kaski, K. (2005). Intensity and coherence of motifs in weighted complex networks. Physical Review E, 71(6), 065103.
Ozek, B., Lu, Z., Pouromran, F., & Kamarthi, S. (2022). Review and Analysis of Pain Research Literature through Keyword Co-occurrence Networks (arXiv:2211.04289). arXiv.
Ren, J., Ren, R., Green, M., & Huang, X. (2019). A Deep Learning Method for Fault Detection of Autonomous Vehicles. 2019 14th International Conference on Computer Science & Education (ICCSE), 749–754.
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why Should I Trust You?”: Explaining the Predictions of Any Classifier (arXiv:1602.04938; Version 3). arXiv.
Shi, Q., & Zhang, H. (2021). Fault Diagnosis of an Autonomous Vehicle With an Improved SVM Algorithm Subject to Unbalanced Datasets. IEEE Transactions on Industrial Electronics, 68(7), 6248–6256.
Singh, S. (2020, August 5). Top 20 Post-Covid Automotive Trends. Forbes.
Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G. (2021). Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability Engineering & System Safety, 215, 107864.
Tong, W., Hussain, A., Bo, W. X., & Maharjan, S. (2019). Artificial Intelligence for Vehicle-to-Everything: A Survey. IEEE Access, 7, 10823–10843.
Tuerxun, W., Chang, X., Hongyu, G., Zhijie, J., & Huajian, Z. (2021). Fault Diagnosis of Wind Turbines Based on a Support Vector Machine Optimized by the Sparrow Search Algorithm. IEEE Access, 9, 69307–69315.
Umair, M., Cheema, M. A., Cheema, O., Li, H., & Lu, H. (2021). Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes, Smart Buildings, Smart Cities, Transportation and Industrial IoT. Sensors, 21(11), 3838.
Wang, D., Chen, Y., Shen, C., Zhong, J., Peng, Z., & Li, C. (2022). Fully interpretable neural network for locating resonance frequency bands for machine condition monitoring. Mechanical Systems and Signal Processing, 168, 108673.
Wang, F., Chen, Z., & Song, G. (2020). Monitoring of multi-bolt connection looseness using entropy-based active sensing and genetic algorithm-based least square support vector machine. Mechanical Systems and Signal Processing, 136, 106507.
Wang, Y., Cui, T., Zhang, F., Dong, T., & Li, S. (2016). Fault diagnosis of diesel engine lubrication system based on PSO-SVM and centroid location algorithm. 2016 International Conference on Control, Automation and Information Sciences (ICCAIS), 221–226.
Wang, Z., Yao, L., & Cai, Y. (2020). Rolling bearing fault diagnosis using generalized refined composite multiscale sample entropy and optimized support vector machine. Measurement, 156, 107574.
Wang, Z., Yao, L., Cai, Y., & Zhang, J. (2020). Mahalanobis semi-supervised mapping and beetle antennae search based support vector machine for wind turbine rolling bearings fault diagnosis. Renewable Energy, 155, 1312–1327.
Wang, Z., Yao, L., Chen, G., & Ding, J. (2021). Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals. ISA Transactions, 114, 470–484.
Wu, J., Guo, P., Cheng, Y., Zhu, H., Wang, X.-B., & Shao, X. (2020). Ensemble Generalized Multiclass Support-Vector-Machine-Based Health Evaluation of Complex Degradation Systems. IEEE/ASME Transactions on Mechatronics, 25(5), 2230–2240.
Xia, K., Liu, B., Fu, X., Guo, H., He, S., Yu, W., Xu, J., & Dong, H. (2019). Wavelet entropy analysis and machine learning classification model of DC serial arc fault in electric vehicle power system. IET Power Electronics, 12(15), 3998–4004.
Xiao, Y., Jia, Y., Liu, C., Cheng, X., Yu, J., & Lv, W. (2019). Edge Computing Security: State of the Art and Challenges. Proceedings of the IEEE, 107(8), 1608–1631.
Xu, X., Zhang, N., Yan, Y., Qin, L., & Qian, F. (2018). Smooth iteration online support tension machine algorithm and application in fault diagnosis of electric vehicle extended range. Advances in Mechanical Engineering, 10(12), 1687814018816563.
Xue, Y., Dou, D., & Yang, J. (2020). Multi-fault diagnosis of rotating machinery based on deep convolution neural network and support vector machine. Measurement, 156, 107571.
Yao, L., Fang, Z., Xiao, Y., Hou, J., & Fu, Z. (2021). An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine. Energy, 214, 118866.
Zhang, L., Lin, J., Liu, B., Zhang, Z., Yan, X., & Wei, M. (2019). A Review on Deep Learning Applications in Prognostics and Health Management. IEEE Access, 7, 162415–162438.
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