Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Investigation of Technological Trends in Automotive Fault Prognostic System

자동차 고장예지시스템의 기술동향 연구

  • Ismail, Azianti (Department of Industrial and Management Engineering, Daegu University) ;
  • Jung, Won (Department of Industrial and Management Engineering, Daegu University)
  • Received : 2013.02.05
  • Accepted : 2013.02.25
  • Published : 2013.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Since the basic built-in-test, prognostic health management (PHM) has evolved into more sophisticated and complex systems with advanced warning and failure detection devices. Aerospace and military systems, manufacturing equipment, structural monitoring, automotive electronic systems and telecommunication systems are examples of fields in which PHM has been fully utilized. Nowadays, the automotive electronic system has become more sophisticated and increasingly dependent on accurate sensors and reliable microprocessors to perform vehicle control functions which help to detect faults and to predict the remaining useful life of automotive parts. As the complication of automotive system increases, the need for intelligent PHM becomes more significant. Given enormous potential to be developed lays ahead, this paper presents findings and discussions on the trends of automotive PHM research with the expectation to offer opportunity for further improving the current technologies and methods to be applied into more advanced applications.

Keywords

References

  1. Antory, D., Application of a data driven monitoring technique to diagnose air leaks in an automotive diesel engine : A case study. Mechanical Systems and Signal Processing, 2007, Vol. 21, No. 2, p 795-808. https://doi.org/10.1016/j.ymssp.2005.11.005
  2. Beatrice, C., Guido, C., Napolitano, P., Iorio, S.D., and Giacomo, N.D., Assessment of biodiesel blending detection capability of the on-board diagnostic of the last generation automotive diesel engines. Fuel, 2011, Vol. 90, No. 5, p 2039-2044. https://doi.org/10.1016/j.fuel.2011.01.013
  3. Biagetti, T. and Enrico, S., Automatic diagnostics and prognostics of energy conversion processes via knowledgebased systems. Energy, 2004, Vol. 29, No. 12, pp. 2553-2572. https://doi.org/10.1016/j.energy.2004.03.031
  4. Breed, D.S., Tire Pressure Monitoring Using Hall Effect Sensor, United States Patent Application Publication, US2006/0212193A1, 2006.
  5. Breed, D.S., Vehicle Communication Using the Internet, United States Patent Application Publication, US2006/0212194A1, 2006.
  6. Breed, D.S., Vehicle Component Control Methods and Systems Based on Vehicle Stability, United States Patent Application Publication, US2008/0046149A1, 2008.
  7. Breed, D.S., Vehicle Diagnostic or Prognostic Message Transmission Systems and Methods, United States Patent Application Publication, US2008/0161989A1, 2008.
  8. Breed, D.S., Information Management and Monitoring System and Method, United States Patent Application Publication, US2009/0043441A1, 2009.
  9. Breed, D.S., Vehicle Diagnostic and Prognostic Methods and Systems, United States Patent Application Publication, US8019501B2, 2011.
  10. Cheng, S., Azarian, M.H., and Pecht, M.G., Sensor Systems for Prognostics and Health Management. Sensors, 2010, Vol. 10, No. 6, p 5774-5797. https://doi.org/10.3390/s100605774
  11. Cheng, S., Tom, K., Thomas, L., and Pecht, M.A, Wireless sensor system for prognostics and health management. 2010, Sensors Journal, IEEE, Vol. 10, No. 4, p 856-862. https://doi.org/10.1109/JSEN.2009.2035817
  12. Eddahech, A., Briat, O., Woirgard, E., and Vinassa, J.M. Remaining useful life prediction of lithium batteries in calendar ageing for automotive applications. Microelectronics Reliability, 2012, Vol. 52, No. 9-10, p 2438-2442. https://doi.org/10.1016/j.microrel.2012.06.085
  13. Garg, V., Fodera, J., and Shen, Z., Prognostics method and system for hybrid and electric vehicle components, United States, Patent Application Publication, US7558 655B2, 2009.
  14. Ghimire, R., Sankavaram, C., Ghahari, A., Pattipati, K., Ghoneim, Y., Howell, M., and Salman, M., Integrated model-based and data-driven fault detection and diagnosis approach for an automotive electric power steering system, AUTOTESTCON, 2011 IEEE, p 70-77.
  15. Goh, K.M., Tjahjono, B., Baines, T., and Subramaniam, S., A review of research in manufacturing prognostics. In Proceedings of Industrial Informatics, 2006 IEEE International Conference, p 417-422.
  16. Gusikhin, O., Rychtyckyj, N., and Filev, D., Intelligent Systems in the Automotive Industry : Applications and Trends. Knowledge and Information Systems, 2007, Vol. 12, p 147-168. https://doi.org/10.1007/s10115-006-0063-1
  17. Ha, C., Chang, J.H., and Kim, J.H., Estimation of Defect Clustering Parameter Using Markov Chain Monte Carlo. Journal of Society of Korea Industrial and Systems Engineering, 2009, Vol. 32, No. 3, p 99-109.
  18. Holland, S.W., Hierarchical Approach for Health Aware Electronics Modules, United States Patent Application Publication, US2010/0131240A, 2010.
  19. Hu, Y., Yurkovich, S., Guezennec, Y., and Yurkovich, B.J., Electro-thermal battery model identification for automotive applications. Journal of Power Sources, 2011, Vol. 196, No. 1, p 449-457. https://doi.org/10.1016/j.jpowsour.2010.06.037
  20. Jardine, A.K.S., Lin, D., and Banjevic, D., A review on machinery diagnostics and prognostics implementing condition based maintenance. Mechanical System and Signal Processing, 2006, Vol. 20, p 1483-1510. https://doi.org/10.1016/j.ymssp.2005.09.012
  21. Kerkhoff, H.G., Wan, J., and Zhao, Y., Hierarchical Modeling of Automotive Sensor Front-Ends for Structural Diagnosis of Aging Faults, In Mixed-Signals, Sensors and Systems Test Workshop (IMS3TW), 2012 18th International, p 91-96.
  22. Lee, M.D., Lim, I.S., and Kim, E., An Application of Principal Component Analysis in Automobile Body Assembly : Case Study. Journal Of Society of KOREA Industrial and Systems Engineering, 2008, Vol. 31, No. 3, p 125-130.
  23. Lembessis, E., Antonopoulos, G., King, R.E., Halatsis, C., and Torres, J., 'CASSANDRA' : an online expert system for fault prognosis. Proceedings of the 5th CIM Europe Conference, p 371-377, 1989.
  24. Lin, W.C., Litkouhi, B.B., Alrabady, A.I., Murty, B.V., Zhang, X.D., Holland, S.W., Salman, M.A., Debouk, R.I., and Chin. Y.W., Autonomous and remote vehicle maintenance and repair', United States Patent Application Publication, US 8190322B2, 2010.
  25. Luo, J., Namburu, M., Pattipati, K, Qia, L., and Chigusa, S., Integrated model-based and data-driven diagnosis of automotive anti-lock braking systems, IEEE System, Man, and Cybernetics-Part A : Systems and Humans IEEE Transactions on, Vol. 40, No.2, p 321-336.
  26. Luo, J., Pattipati, K., Qiao, L. and Chigusa, S., Model-Based Prognostic Techniques Applied to a Suspension System, IEEE System, Man, and Cybernetics-Part A : Systems and Humans IEEE Transactions on, 2008, Vol. 38, No. 5, p 1156-1168. https://doi.org/10.1109/TSMCA.2008.2001055
  27. Medasani, S., Jiang, Q., Srivinasa, N., Zhang, Y., Barajas, L.G., and Kapsokavathias, V.S., Method for anomaly prediction of battery parasitic load, United States Patent, US7761389 B2, 2010.
  28. Meissner, E. and Richter, G., The challenge to the automotive battery industry : the battery has become an increasingly integrated component within the vehicle electric power system. Journal of Power Sources, 2005, Vol. 144, No. 2, p 438-460. https://doi.org/10.1016/j.jpowsour.2004.10.031
  29. Namduri, C.S., Albertson, W.C., and Mc Donald, M.M., Method for Vehicle Suspension Wear Prediction and Indication, United States Patent Application Publication, US 7941256B2, 2011.
  30. Pecht, M., Prognostics and health management of electronics, New York (NY), Wiley-Inter science, 2008.
  31. Pecht, M. and Jaai, R., A prognostics and health management roadmap for information and electronics-rich systems. Microelectronics Reliability, 2010, Vol. 50, No. 3, p 317-323. https://doi.org/10.1016/j.microrel.2010.01.006
  32. Peng, Y., Dong, M., and Zuo, M.J., Current status of machine prognostics in condition-based maintenance : a review. Int Journal Advanced Manufacturing Technology, 2010, Vol. 50, p 297-313. https://doi.org/10.1007/s00170-009-2482-0
  33. Scacchioli, A., Rizzoni, G., and Pisu, P., Hierarchical Model-Based Fault Diagnosis for an Electrical Power Generation Storage Automotive System. In Proceedings of 26th American Control Conference, New York City, p 2991-2996..
  34. Schmidt, A.P., Bitzer, M., Imre, A.W., and Guzzella, L., Model-based distinction and quantification of capacity loss and rate capability fade in Li-ion batteries. Journal of Power Sources, 2010, Vol. 195, No. 22, p 7634-7638. https://doi.org/10.1016/j.jpowsour.2010.06.011
  35. Schneider, M., Ilgin, S., Jegenhorst, N., Kube, R., Puttjer, S., Riemschneider, K., and Vollmer, J., Automotive battery monitoring by wireless cell sensors. In Instrumentation and Measurement Technology Conference (I2MTC), 2012 IEEE International, p 816-820, IEEE. 2012.
  36. Serrao, L., Onori, S., Rizzoni, G., and Guezennec, Y., Model based strategy for estimation of the residual life of automotive batteries. In Proceedings of the 7th IFAC International Symposium on Fault Detection, Supervision and Safety of Technical Processes, Barcelona, June 2009.
  37. Sikorza, J.Z., Hodkiewicz, M., and Ma, L., Prognostic modeling options for remaining useful life estimation by industry'. Mechanical system and Signal Processing, 2011, Vol. 25, p 1803-1836. https://doi.org/10.1016/j.ymssp.2010.11.018
  38. Sollenskog, R., Performance Disc Brake System, United States Patent Application Publication, US 2009/02118 56A1, 2009.
  39. Wang, M.H., Chao, K.H., Sung, W.T., and Huang, G.J., Using ENN-1 for fault recognition of automotive engine. Expert Systems with Applications, 2010, Vol. 37, No. 4, p 2943-2947. https://doi.org/10.1016/j.eswa.2009.09.041
  40. Wei He, Williard, N., Osterman, M., and Pecht, M., Prognostics of lithium-ion batteries based on Dempster- Shafer theory and the Bayesian Monte Carlo method. Journal of Power Sources, 2011, Vol. 196, No. 23, p 10314-10321. https://doi.org/10.1016/j.jpowsour.2011.08.040
  41. Zhang, Battery State of Health Monitoring System and Method, United States, Patent Application Publication, US 2009/0265125 A1, 2009.
  42. Zhang, X., Grube, R., Shin, K., Salman, M., and Conell, R., Parity-relation-based state-of-health monitoring of lead acid batteries for automotive applications. Control Engineering Practice, 2011, Vol. 19, No. 6, p 555-563. https://doi.org/10.1016/j.conengprac.2010.05.014
  43. Zhang, X.D., Lin, W.C., Zhang, Y.L., Salman, M.A., Chin, Y.K., Holland, S.W., and Howell, M.N., Proactive Vehicle Management System And Maintenance By Using Diagnostic and Prognostics Information, United States Patent Application Publication, US 2010/00422287 A1, 2010.
  44. Zhang, Y., Gantt, G.W., Rychlinski, M.J., Edwards, R.M., Correia, J.J., and Wolf, C.E., Connected vehicle diagnostics and prognostics, concept, and initial practice, Reliability. IEEE Transactions on, 2009, Vol. 58, No. 2, p 286-294.
  45. Zhou, Y. and Gorman, G., Elimination of Errors Due to Aging In Magneto-Resistive Devices, United States, Patent Application Publication, US 8203337 B2, 2012.