Open Access
MATEC Web of Conferences
Volume 30, 2015
2015 the 4th International Conference on Material Science and Engineering Technology (ICMSET 2015)
Article Number 04003
Number of page(s) 6
Section Mechanical design and manufacturing
Published online 04 November 2015
  1. R. Isermann, Fault-Diagnosis Applications: Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-tolerant Systems: Springer Berlin Heidelberg, 2011.
  2. M. Muenchhof, “Comparison of change detection methods for a residual of a hydraulic servo-axis,” pp. 1854–1854, 2005.
  3. C. W. Chan, et al., “Application of Fully Decoupled Parity Equation in Fault Detection and Identification of DC Motors,” Industrial Electronics, IEEE Transactions on, vol. 53, pp. 1277–1284, 2006. [CrossRef]
  4. T. Escobet and L. Trave-Massuyes, “ Parameter estimation methods for fault detection and isolation,” Bridge Workshop Notes, 2001.
  5. M. Hilbert, et al., “Observer Based Condition Monitoring of the Generator Temperature Integrated in the Wind Turbine Controller,” EWEA 2013 Scientific Proceedings: Vienna, 4-7 February 2013, pp. 189–193, 2013.
  6. G. Heredia and A. Ollero, “Sensor fault detection in small autonomous helicopters using observer/Kalman filter identification,” in Mechatronics, 2009. ICM 2009. IEEE International Conference on, 2009, pp. 1–6.
  7. N. Meskin and K. Khorasani, Fault detection and isolation: multi-vehicle unmanned systems. New York: Springer, 2011. [CrossRef]
  8. H. A. Aldridge, “Robot position sensor fault tolerance,” Ph.D. 9713717, Carnegie Mellon University, United States Pennsylvania, 1996.
  9. LawrenceP. J., Jr. and M. P. Berarducci, “Comparison of federated and centralized Kalman filters with fault detection considerations,” in Position Location and Navigation Symposium, 1994., IEEE, 1994, pp. 703–710.
  10. N. A. Carlson, “Federated filter for fault-tolerant integrated navigation systems,” in Position Location and Navigation Symposium, 1988. Record. Navigation into the 21st Century. IEEE PLANS ‘88., IEEE, 1988, pp. 110–119.
  11. A. Edelmayer and M. Miranda, “Federated filtering for fault tolerant estimation and sensor redundancy management in coupled dynamics distributed systems,” in Control & Automation, 2007. MED ‘07. Mediterranean Conference on, 2007, pp. 1–6.
  12. T. Xu, et al., “A multi-sensor data fusion navigation system for an unmanned surface vehicle,” Proceedings of the Institution of Mechanical Engineers, vol. 221, pp. 167–175, 177-186, 2007.
  13. L. Xu and Z. Weigong, “An Adaptive Fault-Tolerant Multisensor Navigation Strategy for Automated Vehicles,” Vehicular Technology, IEEE Transactions on, vol. 59, pp. 2815–2829, 2010. [CrossRef]
  14. D. Fengyang, et al., “Study on Fault-tolerant Filter Algorithm for Integrated Navigation System,” in Mechatronics and Automation, 2007. ICMA 2007. International Conference on, 2007, pp. 2419–2423.
  15. F. E. White, “Data Fusion Lexicon” JOINT DIRECTORS OF LABS WASHINGTON DC. 1991.
  16. A. N. Steinberg, et al., “Revisions to the JDL data fusion model,” Sensor Fusion: Architectures, Algorithms, and Applications III, vol. 3719, pp. 430–441, 1999. [CrossRef]
  17. A. Polychronopoulos and A. Amditis, “Revisiting JDL model for automotive safety applications: the PF2 functional model,” in Information Fusion, 2006 9th International Conference on, 2006, pp. 1–7.
  18. M. Realpe, et al., “Towards Fault Tolerant Perception for autonomous vehicles: Local Fusion,” presented at the 7th IEEE International Conference on Robotics, Automation and Mechatronics (RAM), Angkor Wat – Cambodia, 2015.
  19. A. Geiger, et al., “Are we ready for autonomous driving? The KITTI vision benchmark suite,” in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, 2012, pp. 3354–3361.
  20. A. Geiger, et al., “Vision meets robotics: The KITTI dataset,” The International Journal of Robotics Research, vol. 32, pp. 1231–1237, September 1, 2013 2013. [CrossRef]
  21. J. Fritsch, et al., “A new performance measure and evaluation benchmark for road detection algorithms,” in Intelligent Transportation Systems - (ITSC), 2013 16th International IEEE Conference on, 2013, pp. 1693–1700.
  22. T. Joachims, “Making large-scale support vector machine learning practical,” in Advances in kernel methods, ed: MIT Press, 1999, pp. 169–184.
  23. V. Kecman, Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models: MIT Press, 2001.