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Automotive IVHM: Towards Intelligent Personalised Systems Healthcare

Published online by Cambridge University Press:  26 July 2019

Felician Campean*
Affiliation:
University of Bradford, Automotive Research Centre;
Daniel Neagu
Affiliation:
University of Bradford, Automotive Research Centre;
Aleksandr Doikin
Affiliation:
University of Bradford, Automotive Research Centre;
Morteza Soleimani
Affiliation:
University of Bradford, Automotive Research Centre;
Thomas Byrne
Affiliation:
University of Bradford, Automotive Research Centre;
Andrew Sherratt
Affiliation:
Jaguar Land Rover
*
Contact: Campean, Felician, University of Bradford, School of Engineering, United Kingdom, F.Campean@bradford.ac.uk

Abstract

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Underpinned by a contemporary view of automotive systems as cyber-physical systems, characterised by progressively open architectures increasingly defined by their interaction with the users and the smart environment, this paper provides a critical and up-to-date review of automotive Integrated Vehicle Health Management (IVHM) systems. The paper discusses the challenges with prognostics and intelligent health management of automotive systems, and proposes a high-level framework, referred to as the Automotive Healthcare Analytic Factory, to systematically collect and process heterogeneous data from across the product lifecycle, towards actionable insight for personalised healthcare of systems.

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) 2019

References

Benedetini, O., Baines, T. S., Lightfoot, H. W. and Greenough, R. M. (2009), “State-of-the-art in integrated vehicle health management”. Proc IMechE, Part G: Journal of Aerospace Eng, Vol. 223, pp. 157170.Google Scholar
Byrne, T. J., Campean, F. and Neagu, D. (2018), “Towards a framework for engineering big data: An automotive systems perspective”, International Design Conference - Design 2018. http://doi.org/10.21278/idc.2018.0490Google Scholar
Daigle, M. and Goebel, K. (2011), “A Model Based Prognostics applied to Pneumatic Valves”, Int Jrnl of Prognostics & Health Management, Vol. 2 No. 2, pp. 116.Google Scholar
Davis, T. (2003), “Reliability improvement in automotive engineering”. In Global Vehicle Reliability - Prediction and Optimization Techniques, Strutt, JE. and Hall, PL (Eds), PEP, London.Google Scholar
Doikin, A., Zadeh, E. H., Campean, F., Priest, M., Brown, M. and Sherratt, A. (2018), “Impact of duty cycle on wear progression in variable displacement vane oil pumps”, Procedia Manufacturing, Vol. 16, pp. 115122, http://doi.org/10.1016/j.promfg.2018.10.170Google Scholar
Elattar, H.M., Elminir, H.K. and Riad, A.M. (2016), “Prognostics: a Literature Review”, Complex Intell. Syst., Vol. 2, pp. 125154, http://doi.org/10.1007/s40747-016-0019-3.Google Scholar
Felke, T., Holland, S. and Raviram, S. (2017), “Integration of Component Design Data for Automotive Turbocharger with Vehicle Fault Model Using JA6268 Methodology”, SAE Int. J. Passeng. Cars – Electron. Electr. Syst., Vol. 10 No. 2, 2017, http://doi.org/10.4271/2017-01-1623.Google Scholar
Fink, O., Zio, E. and Weidmann, U. (2015), “A classification framework for predicting components remaining useful life based on discrete event diagnostic data”, IEEE Transactions on Reliability, Vol. 64 No. 3, pp. 10491056.10.1109/TR.2015.2440531Google Scholar
Holland, S.W. (2010), “Integrated Vehicle Health management in the Auto Industry”, in Smigorski, K. (Editor) Health Management, IntechOpen, ISBN: 978-953-307-120-6.Google Scholar
Kim, N-H., An, D. and Choi, J-H. (2017), Prognostics and Health Management of Engineering Systems, Springer, http://doi.org/10.1007/978-3-319-44742-1.Google Scholar
Kimita, K., Sakao, T. and Shimomura, Y. (2018), “A failure analuysis method for designing highly reliable product-service systems”, Res Eng Design, Vol. 29, pp. 143160, http://doi.org/10.1007/s00163-017-0261-8.Google Scholar
Kritzinger, W., Karner, M., Traar, G., Henjes, J. and Sihn, W. (2018), “Digital Twin in manufacturing: A categorical literature review and classification”, IFAC PapersOnLine, Vol. 51 No. 11, pp. 10161022, http://doi.org/10.1016/j.ifacol.2018.08.474Google Scholar
Lee, J. et al. (2014), “Prognostics and Health Management design for rotary machinery systems - Reviews, methodology and applications”, Mechanical Systsems and Signal Processing, Vol. 42, pp. 314334. http://doi.org/10.1016/j.ymssp.2013.06.004Google Scholar
Liu, B., Yan, F., Hu, J., Turkson, R.F. and Lin, F. (2016), “Modeling and Multi-Objective Optimization of NOx Conversion Efficiency and NH3 Slip for a Diesel Engine”, Sustainability, Vol. 8 No. 5, pp. 478491, http://doi.org/10.3390/su8050478.Google Scholar
Mosallam, A., Medjaher, K. and Zerhouni, N. (2016), “Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction”, Jrnl. Intel. Manuf., Vol. 27 No. 5, pp. 10371048.10.1007/s10845-014-0933-4Google Scholar
Negri, E., Fumagalli, L. and Macchi, M. (2017), “A review of the roles of Digital Twin in CPS-based production systems”, Procedia Manufacturing, Vol. 11 No. 2017, pp. 939948, http://doi.org/10.1016/j.promfg.2017.07.198.Google Scholar
Prytz, R., Nowaczyk, S., Rögnvaldsson, T. and Byttner, S. (2016), “Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data”, Eng Appl of Artificial Intelligence, Vol. 41, pp. 139150.10.1016/j.engappai.2015.02.009Google Scholar
Rosen, R., Von Wichert, G., Lo, G. and Bettenhausen, K. D. (2015), “About The Importance of Autonomy and Digital Twins for the Future of Manufacturing”, IFAC PapersOnLine, Vol. 48, pp. 567572, http://doi.org/10.1016/j.ifacol.2015.06.141.Google Scholar
Roychoudhury, I., Daigle, M.J., Bregon, A. and Pulido, B. (2013), “A Structural Model Decomposition Framework for System Health Management”, Proc IEEE Aerospace Conf, Big Sky, MT. http://doi.org/10.1109/AERO.2013.6496975Google Scholar
SAE International (2018), Design & Run-Time Information Exchange for Health-Ready Components, Surface Vehicles / Aerospace Recommended Practice JA6268 2018-04.Google Scholar
Sankararaman, S., Daigle, M.J. and Goebel, K. (2014), “Uncertainty Quantification in remaining Useful Life Prediction Using First Order Reliability Methods”, IEEE Trans Reliability, Vol. 63 No. 2, pp. 603619.10.1109/TR.2014.2313801Google Scholar
Schleich, B., Anwer, N., Mathieu, L. and Wartzack, S. (2017), “Shaping the digital twin for design and production engineering”, Cirp Annals-Manufacturing Technology, Vol. 66, pp. 141144.10.1016/j.cirp.2017.04.040Google Scholar
Soleimani, M., Campean, F. and Neagu, D. (2018), “Reliability Challenges for Automotive Aftertreatment Systems: a State-of-the-art Perspective”, Procedia Manufacturing, Vol. 16, pp. 7582, http://doi.org/10.1016/j.promfg.2018.10.174.Google Scholar
Viceconti, M., Hunter, P. and Hose, R. (2015), “Big Data, Big Knowledge: Big data for Personalised Healthcare”, IEEE Journal of Biomedical and Health Informatics, Vol. 19 No. 4, pp. 12091215, http://doi.org/10.1109/JBHI.2015.2406883.Google Scholar
Wei, Z., Rebandt, R., Start, M., Gao, L., Hamilton, J. and Luo, L. (2015), “Approaches to Achieving High Reliability and Confidence Levels with Small Test Sample Sizes”. SAE International Journal of Commercial Vehicles, Vol. 8, pp. 343354, http://doi.org/10.4271/2016-01-0269.Google Scholar
Weyer, S., Meyer, T., Ohmer, M., Gorecky, D. and Zuhlke, D. (2016), “Future Modelling and Simulation of CPS-based factories: an Example from the Automotive Industry”, IFAC PapersOnLine, pp. 49–31, 97102, http://doi.org/10.1016/j.ifacol.2016.12.168.Google Scholar
Wilmering, T. (2017), “Integrated Vehicle Health management - System of Systems Integration”, SAE International, ISBN 978-0-7680-8428-3.10.4271/PT-182Google Scholar