차량 EMB 시스템의 고장 검출 및 대처 방안 설계

  • 금대현 (대구경북과학기술원 미래자동차융합연구센터) ;
  • 반동훈 (대구경북과학기술원 미래자동차융합연구센터) ;
  • 권수현 (대구경북과학기술원 미래자동차융합연구센터) ;
  • 진성호 (대구경북과학기술원 미래자동차융합연구센터) ;
  • 이성훈 (대구경북과학기술원 미래자동차융합연구센터)
  • Published : 2017.04.28

Abstract

Electromechanical Brake(EMB)시스템은 유압 대신 전동식 액츄에이터를 이용하여 제동력을 발생시키는 브레이크 장치인 Brake-by-Wire(BBW) 시스템의 구성요소 이다. EMB 시스템은 기존 유압식 브레이크 시스템과 비교하여 친환경적이며, 설계의 자유도가 높고, 제동 응답성 및 제어 성능이 뛰어난 장점을 가진다. 하지만 전자 전기적으로 시스템 구성이 복잡해 짐에 따라 고장에 대한 안정성 부문이 설계시 충분히 고려되어야 한다. 본 논문에서는 차량 EMB 시스템 설계시 신뢰성을 향상을 위해 고려해야 설계 방안에 대해서 기술한다. 크게 시스템, 센서, 모터 분야에 대해 고장 요소 및 대처 방안 설계에 대해 개괄적으로 소개한다.

Keywords

References

  1. 현대모비스, www.mobis.co.kr
  2. T. Mei, and X. Ding, 2008. A Model-less Technique for the Fault Detection of Rail Vehicle Suspensions. Vehicle System Dynamics, 46, pp.277-287. https://doi.org/10.1080/00423110801939154
  3. J. Castillo, J. Cabrera, A. Guerra, and A. Simon, 2016. A Novel Electrohydraulic Brake System with Tire-Road Friction Estimation and Continuous Brake Pressure Control. IEEE Transactions on Industrial Electronics, 63(3), pp.1863-1875. https://doi.org/10.1109/TIE.2015.2494041
  4. Su, S.Y.H. and DuCasse, E., 1980. A hardware redundancy reconfiguration scheme for tolerating multiple module failures. IEEE Transactions on Computers, 29(3), pp.254-258.
  5. Sheaffer, J.W., Luebke, D.P. and Skadron, K., 2007, August. A hardware redundancy and recovery mechanism for reliable scientific computation on graphics processors. In Graphics Hardware (Vol. 2007, pp. 55-64).
  6. Anwar, S. and Chen, L., 2007. An analytical redundancy-based fault detection and isolation algorithm for a road-wheel control subsystem in a steer-by-wire system. IEEE Transactions on Vehicular Technology, 56(5), pp.2859-2869. https://doi.org/10.1109/TVT.2007.900515
  7. Huang, S., Tan, K.K. and Lee, T.H., 2012. Fault diagnosis and fault-tolerant control in linear drives using the Kalman filter. IEEE Transactions on Industrial Electronics, 59(11), pp.4285-4292. https://doi.org/10.1109/TIE.2012.2185011
  8. Hwang, W., Huh, K., Kim, M. and Jung, J., 2012. Sensor Fault Diagnosis for EMB using Parity Space Approach (No. 2012-01-1794). SAE Technical Paper.
  9. Ge, Z., Song, Z. and Gao, F., 2013. Review of recent research on data-based process monitoring. Industrial & Engineering Chemistry Research, 52(10), pp.3543-3562. https://doi.org/10.1021/ie302069q
  10. Kadlec, P., Grbic, R. and Gabrys, B., 2011. Review of adaptation mechanisms for data-driven soft sensors. Computers & chemical engineering, 35(1), pp.1-24. https://doi.org/10.1016/j.compchemeng.2010.07.034
  11. Nodland, D., Zargarzadeh, H. and Jagannathan, S., 2013. Neural network-based optimal adaptive output feedback control of a helicopter UAV. IEEE transactions on neural networks and learning systems, 24(7), pp.1061-1073. https://doi.org/10.1109/TNNLS.2013.2251747
  12. Widodo, A. and Yang, B.S., 2007. Support vector machine in machine condition monitoring and fault diagnosis. Mechanical systems and signal processing, 21(6), pp.2560-2574. https://doi.org/10.1016/j.ymssp.2006.12.007
  13. Kim, K., Li, Q., Park, C., Hwang, K., Kim, J. and Kim, H., 2011. A design of intelligent actuator logic using fuzzy control for EMB system. In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 2).
  14. Freeman, P., Pandita, R., Srivastava, N. and Balas, G.J., 2013. Model-based and data-driven fault detection performance for a small UAV. IEEE/ASME Transactions on mechatronics, 18(4), pp.1300-1309. https://doi.org/10.1109/TMECH.2013.2258678
  15. Cheng, Y., Wang, R. and Xu, M., 2016. A Combined Model-Based and Intelligent Method for Small Fault Detection and Isolation of Actuators. IEEE Transactions on Industrial Electronics, 63(4), pp.2403-2413. https://doi.org/10.1109/TIE.2015.2499722
  16. Rong, H., Lv, J., Peng, C., Zou, L., Ma, Z., Chen, Y. and Zhu, Y., 2016. Dynamic Regulation of the Weights of Request Based on the Kalman Filter and an Artificial Neural Network. IEEE Sensors Journal, 16(23), pp.8597-8607. https://doi.org/10.1109/JSEN.2016.2611610
  17. Rubaai, A. and Young, P., 2016. Hardware/ Software Implementation of Fuzzy-Neural-Network Self-Learning Control Methods for Brushless DC Motor Drives. IEEE Transactions on Industry Applications, 52(1), pp.414-424. https://doi.org/10.1109/TIA.2015.2468191
  18. Zhang, Y. and Jiang, J., 2008. Bibliographical review on reconfigurable fault-tolerant control systems. Annual reviews in control, 32(2), pp.229-252. https://doi.org/10.1016/j.arcontrol.2008.03.008
  19. Ki, Y.H., Lee, K.J., Cheon, J.S. and Ahn, H.S., 2013. Design and implementation of a new clamping force estimator in electro-mechanical brake systems. International Journal of Automotive Technology, 14(5), pp.739-745. https://doi.org/10.1007/s12239-013-0081-4
  20. Lee, Y.O., Son, Y.S. and Chung, C.C., 2013. Clamping force control for an electric parking brake system: switched system approach. IEEE transactions on vehicular technology, 62(7), pp.2937-2948. https://doi.org/10.1109/TVT.2013.2251029
  21. Jo, C., Hwang, S. and Kim, H., 2010. Clampingforce control for electromechanical brake. IEEE Transactions on Vehicular Technology, 59(7), pp.3205-3212. https://doi.org/10.1109/TVT.2010.2043696
  22. D. H. Ban, S. H. Jin, and J. S. Hong, "The Design and Implementation of a Fault diagnosis on a Motor Control System," Conference of KSAE 2014, pp.1081-1086
  23. D. H. Ban, J. S. Hong, and S. H. Lee, "Diagnostic of the inverter / the motor using DC current," Conference of ICROS 2016, pp. 135-136
  24. D. H. Ban, J. S. Hong, and S. H. Lee, "Rotor Position Estimation of Permanent Magnet Synchronous Motors Using Low-Resolution Sensors," International Journal of Fuzzy Systems, vol. 19, no. 1, pp. 78-85
  25. J. Zambada, and D. Deb, "Sensorless Field Oriented Control of a PMSM," AN1078, Microchip Technology Inc.