Hostname: page-component-848d4c4894-tn8tq Total loading time: 0 Render date: 2024-06-22T13:00:59.210Z Has data issue: false hasContentIssue false

A RELIABILITY INSPIRED STRATEGY FOR INTELLIGENT PERFORMANCE MANAGEMENT WITH PREDICTIVE DRIVER BEHAVIOUR: A CASE STUDY FOR A DIESEL PARTICULATE FILTER

Published online by Cambridge University Press:  27 July 2021

Aleksandr Doikin
Affiliation:
University of Bradford
Felician Campean*
Affiliation:
University of Bradford
Martin Priest
Affiliation:
University of Bradford
Chunxing Lin
Affiliation:
Jaguar Land Rover
Emanuele Angiolini
Affiliation:
Jaguar Land Rover
*
Campean, Felician, University of Bradford, School of Engineering, United Kingdom, F.Campean@bradford.ac.uk

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The increase availability of operational data from the fleets of cars in the field offers opportunities to deploy machine learning to identify patterns of driver behaviour. This provides contextual intelligence insight that can be used to design strategies for online optimisation of the vehicle performance, including compliance with stringent legislation. This paper illustrates this approach with a case study for a Diesel Particulate Filter, where machine learning deployed to real world automotive data is used in conjunction with a reliability inspired performance modelling paradigm to design a strategy to enhance operational performance based on predictive driver behaviour. The model-in-the-loop simulation of the proposed strategy on a fleet of vehicles showed significant improvement compared to the base strategy, demonstrating the value of the approach.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

References

Alaswad, S. & Xiang, Y. 2017. A review on condition-based maintenance optimization models for stochastically deteriorating system. Rel Eng & System Safety, 157, 5463. doi: https://doi.org/10.1016/j.ress.2016.08.009.CrossRefGoogle Scholar
Atamuradov, V., Medjaher, K., Dersin, P., Lamoureux, B. & Zerhouni, N. 2017. Prognostics and health management for maintenance practitioners-review, implementation and tools evaluation. International Journal of Prognostics and Health Management, 8, 131.Google Scholar
Bai, S., Tang, J., Wang, G. & Li, G. 2016. Soot loading estimation model and passive regeneration characteristics of DPF system for heavy-duty engine. Applied Thermal Engineering, 100, 12921298. doi: https://doi.org/10.1016/j.applthermaleng.2016.02.055.CrossRefGoogle Scholar
Barba, F., Vassallo, A. & Greco, V. Estimation of DPF Soot Loading through Steady-State Engine Mapping and Simulation for Automotive Diesel Engines Running on Petroleum-Based Fuels. 2017. SAE International. doi: https://doi.org/10.4271/2017-24-0139.CrossRefGoogle Scholar
Bsi Standards. BS EN 13306:2017: Maintenance. Maintenance terminology. 2017. British Standards Institute.Google Scholar
Campean, F., Neagu, D., Doikin, A., Soleimani, M., Byrne, T. & Sherratt, A. Automotive IVHM: Towards Intelligent Personalised Systems Healthcare. Proc Design Society: International Conference on Engineering Design, 2019. Cambridge University Press, 857866. doi: https://doi.org/10.1017/dsi.2019.90.CrossRefGoogle Scholar
Castellano, J., Chaudhari, A. & Bromham, J. Adaptive Temperature Control for Diesel Particulate Filter Regeneration. 2013. SAE International. doi: https://doi.org/10.4271/2013-01-0517.Google Scholar
Chen, K., Martirosyan, K. S. & Luss, D. 2011. Temperature gradients within a soot layer during DPF regeneration. Chem Eng Science, 66, 29682973. doi: https://doi.org/10.1016/j.ces.2011.03.037.CrossRefGoogle Scholar
Dawei, Q., Jun, L. & Yu, L. 2017. Research on particulate filter simulation and regeneration control strategy. Mechanical Systems and Signal Processing, 87, 214226. doi: https://doi.org/10.1016/j.ymssp.2016.05.039.CrossRefGoogle Scholar
Doikin, A., Campean, F., Neagu, D., Priest, M., Soleimani, M. & Lin, C. Knowledge-Enabled Machine Learning for Predictive Diagnostics: A Case Study for an Automotive Diesel Particulate Filter. Proceeding 30th ESREL Conference and 15th PSAM Conference, 2020.CrossRefGoogle Scholar
Eker, Ö. F., Camci, F. & Jennions, I. K. Major challenges in prognostics: study on benchmarking prognostic datasets. 1st European Conf Prognostics and Health Mt Society, 2012 Dresden, Germany. PHM Society.Google Scholar
Ellis, B. A. 2008. Condition Based Maintenance. The Jethro Project, 15.Google Scholar
Howell, M. A., Fadi, S. 2017. Different Maintenance Types and The Need for Energy Centered Maintenance. Energy Centered Maintenance - A Green Maintenance System. Fairmont Press, Inc.Google Scholar
Ito, T., Kitamura, T., Kojima, H. & Kawanabe, H. Prediction of Oil Dilution by Post-injection in DPF Regeneration Mode. 2019. SAE International. doi: https://doi.org/10.4271/2019-01-2354.CrossRefGoogle Scholar
Javed, K., Gouriveau, R. & Zerhouni, N. 2017. State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels. Mechanical Systems and Signal Processing, 94, 214236. doi: https://doi.org/10.1016/j.ymssp.2017.01.050.CrossRefGoogle Scholar
Kobbacy, K. a. H. & Murthy, D. P. 2008. Complex system maintenance handbook, Springer Science&Business.Google Scholar
Kotrba, A., Bai, L., Yetkin, A., Shotwell, R. & Gardner, T. DPF Regeneration Response: Coupling Various DPFs with a Thermal Regeneration Unit to Assess System Behaviors. 2011. SAE International. https://doi.org/10.4271/2011-01-2200.Google Scholar
Larsen, J. M., Christodoulou, L., Calcaterra, J. R., Dent, M. L., Derriso, M. M., Hardman, W. J., Jones, J. W. & Russ, S. M. 2017. Integrated Systems Health Management: Enabling Technology for Effective Utilization of Air Vehicle Systems. Materials Damage Prognosis. TMS (The Minerals, Metals & Materials Society).Google Scholar
Majewski, W. A. 2005. Wall-Flow Monoliths [Online]. DieselNet Technology Guide. Available: https://dieselnet.com/tech/dpf_wall-flow.php [Accessed 10/05/2020].Google Scholar
Mimosa 2010. OSA-CBM V3.3.1 UML Model. Normative Information Specification.Google Scholar
Mounce, S., Ellis, K., Edwards, J., Speight, V., Jakomis, N. & Boxall, J. 2017. Ensemble decision tree models using RUSBoost for estimating risk of iron failure in drinking water distribution systems. Water Resources Management, 31, 15751589. doi: https://doi.org/10.1007/s11269-017-1595-8.CrossRefGoogle Scholar
Nguyen, V., Kefalas, M., Yang, K., Apostolidis, A., Olhofer, M., Limmer, S. & Bäck, T. 2019. A Review: Prognostics and Health Management in Automotive and Aerospace. International Journal of Prognostics and Health Management, 10. doi: https://doi.org/10.36001/ijphm.2019.v10i2.2730.Google Scholar
Petryna, Y., Link, M. & Künzel, A. Modeling and Monitoring of Damage in Grouted Joints. 6th European Workshop on Structural Health Monitoring, 2012.Google Scholar
Prajapati, A. K. & Roy, B. K. A State of Art Review of Integrated Vehicle Health Management System. 2018 3rd International Conference for Convergence in Technology (I2CT), 2018. IEEE, 15. https://doi.org/10.1109/I2CT.2018.8529590.CrossRefGoogle Scholar
Rebello, S., Yu, H. & Ma, L. 2018. An integrated approach for system functional reliability assessment using Dynamic Bayesian Network and Hidden Markov Model. Reliability Engineering & System Safety. https://doi.org/10.1016/j.ress.2018.07.002.CrossRefGoogle Scholar
Sae International. Design & Run-Time Information Exchange for Health-Ready Components. 2018. https://doi.org/10.4271/JA6268_201804.CrossRefGoogle Scholar
Saha, B. & Goebel, K. Uncertainty management for diagnostics and prognostics of batteries using Bayesian techniques. 2008 IEEE Aerospace Conf, 18. https://doi.org/10.1109/AERO.2008.4526631.CrossRefGoogle Scholar
Sankararaman, S. 2015. Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction. Mechanical Systems and Signal Processing, 52, 228247. https://doi.org/10.1016/j.ymssp.2014.05.029.CrossRefGoogle Scholar
Seiffert, C., Khoshgoftaar, T. M., Van Hulse, J. & Napolitano, A. 2010. RUSBoost: A hybrid approach to alleviating class imbalance. IEEE Trans Systems, Man, and Cybernetics-Part A: Systems and Humans, 40, 185197. https://doi.org/10.1109/TSMCA.2009.2029559.CrossRefGoogle Scholar
Shaojun, H., Jin, C., Ruixu, G. & Guijun, W. The Capability Analysis on the Characteristic Selection Algorithm of Text Categorization Based on F1 Measure Value. 2nd Int Conf on Instrum, Measurement, Computer, Communication and Control, 2012. IEEE, 742746. https://doi.org/10.1109/IMCCC.2012.180.CrossRefGoogle Scholar
Sikorska, J., Hodkiewicz, M. & Ma, L. 2011. Prognostic modelling options for remaining useful life estimation by industry. Mech sys & signal proc, 25, 18031836. https://doi.org/10.1016/j.ymssp.2010.11.018.CrossRefGoogle Scholar
Singh, N., Rutland, C. J., Foster, D. E., Narayanaswamy, K. & He, Y. Investigation into Different DPF Regeneration Strategies Based on Fuel Economy Using Integrated System Simulation. 2009. SAE International. https://doi.org/10.4271/2009-01-1275.Google Scholar
Tamssaouet, F., Nguyen, K. T., Medjaher, K. & Orchard, M. E. 2020. Degradation Modeling and Uncertainty Quantification for System-Level Prognostics. IEEE Systems https://doi.org/10.1109/JSYST.2020.2983376.CrossRefGoogle Scholar
Tong, D., Zhang, J., Wang, G., Yang, B., Cai, K., Liu, S., Abdalla, A. & Shuai, S.-J. Experimental Study and Numerical Interpretation on the Temperature Field of DPF during Active Regeneration with Hydrocarbon Injection. 2018. SAE International. https://doi.org/10.4271/2018-01-1257.CrossRefGoogle Scholar
Torgunov, D., Trundle, P., Campean, F., Neagu, D. & Sherratt, A. Vehicle Warranty Claim Prediction from Diagnostic Data Using Classification. UKCI Workshop, 2019. Springer, 483492. https://doi.org/10.1007/978-3-030-29933-0_40.Google Scholar