On-Board Predictive Maintenance with Machine Learning
Published April 2, 2019 by SAE International in United States
Downloadable datasets for this paper availableAnnotation of this paper is available
Field Issue (Malfunction) incidents are costly for the manufacturer’s service department. Especially for commercial truck providers, downtime can be the biggest concern for our customers. To reduce warranty cost and improve customer confidence in our products, preventive maintenance provides the benefit of fixing the problem when it is small and reducing downtime of scheduled targeted service time. However, a normal telematics system has difficulty in capturing useful information even with pre-set triggers. Some malfunction issue takes weeks to find the root cause due to the difficulty of repeating the error in a different vehicle and engineers must analyze large amounts of data. In order to solve these challenges, a machine-learning-based predictive software/hardware system has been implemented. Multiple machine learning techniques, including CNN(Convolutional Neural Network), have been utilized in the proposed pipeline to: 1) decide when to record data. 2) decide what data to record. 3) root cause diagnostics on the spot based on time-series data analysis. A novel technique has been proposed to solve the lack of training data for the root cause analysis neural network. The root cause analysis will be further reviewed by engineers through an expert knowledge feedback system to guide the on-board AI. In this paper, the overall on-board preventive maintenance system will be introduced, and validation results will be shown.
CitationSun, Y., Xu, Z., and Zhang, T., "On-Board Predictive Maintenance with Machine Learning," SAE Technical Paper 2019-01-1048, 2019, https://doi.org/10.4271/2019-01-1048.
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- Prytz, R., “Machine Learning Methods for Vehicle Predictive Maintenance Using Off-Board and On-Board Data,” Ph.D. diss., Halmstad University Press, 2014.
- Zhang, Y., Gantt, G.W., Rychlinski, M.J., Edwards, R.M. et al., “Connected Vehicle Diagnostics and Prognostics, Concept, and Initial Practice,” IEEE Transactions on Reliability 58(2):286-294, 2009.
- Susto, G.A., Schirru, A., Pampuri, S., McLoone, S. et al., “Machine Learning for Predictive Maintenance: A Multiple Classifier Approach,” IEEE Transactions on Industrial Informatics 11(3):812-820, 2015.
- Bolón-Canedo, V., Sánchez-Maroño, N., and Alonso-Betanzos, A., “A Review of Feature Selection Methods on Synthetic Data,” Knowledge and Information Systems 34(3):483-519, 2013.
- Frisk, E., Krysander, M., and Larsson, E., “Data-Driven Lead-Acid Battery Prognostics Using Random Survival Forests,” Linkoping University Linkoping, Sweden, 2014.
- Filev, D.P., Chinnam, R.B., Tseng, F., and Baruah, P., “An Industrial Strength Novelty Detection Framework for Autonomous Equipment Monitoring and Diagnostics,” IEEE Transactions on Industrial Informatics 6(4):767-779, 2010.
- Zheng, Y., Liu, Q., Chen, E., Ge, Y., et al., “Time-Series Classification Using Multi-Channels Deep Convolutional Neural Networks,” in International Conference on Web-Age Information Management, 2014, 298-310, Springer, Cham.
- Malhotra, P., Vig, L., Shroff, G., and Agarwal, P., “Long Short Term Memory Networks for Anomaly Detection in Time-Series,” in Proceedings, 2015, 89, Presses Universitaires de Louvain.
- Agarwal, A. and Yadav V., “Advanced Integrated Future Vehicle Telematics System Concept Modelling,” 2017.
- Baumgartner, M., Léonardi, J., and Krusch, O., “Improving Computerized Routing and Scheduling and Vehicle Telematics: A Qualitative Survey,” Transportation Research Part D: Transport and Environment 13(6):377-382, 2008.
- Dietterich, T.G., “Ensemble Methods in Machine Learning,” in International Workshop on Multiple Classifier Systems, 2000, 1-15, Springer, Berlin, Heidelberg.