Open Access
Issue
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
DOI https://doi.org/10.1051/matecconf/20153004003
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. [Google Scholar]
  2. M. Muenchhof, “Comparison of change detection methods for a residual of a hydraulic servo-axis,” pp. 1854–1854, 2005. [Google Scholar]
  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] [Google Scholar]
  4. T. Escobet and L. Trave-Massuyes, “ Parameter estimation methods for fault detection and isolation,” Bridge Workshop Notes, 2001. [Google Scholar]
  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. [Google Scholar]
  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. [Google Scholar]
  7. N. Meskin and K. Khorasani, Fault detection and isolation: multi-vehicle unmanned systems. New York: Springer, 2011. [CrossRef] [Google Scholar]
  8. H. A. Aldridge, “Robot position sensor fault tolerance,” Ph.D. 9713717, Carnegie Mellon University, United States Pennsylvania, 1996. [Google Scholar]
  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. [Google Scholar]
  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. [Google Scholar]
  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. [Google Scholar]
  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. [Google Scholar]
  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] [Google Scholar]
  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. [Google Scholar]
  15. F. E. White, “Data Fusion Lexicon” JOINT DIRECTORS OF LABS WASHINGTON DC. 1991. [Google Scholar]
  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] [Google Scholar]
  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. [Google Scholar]
  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. [Google Scholar]
  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. [Google Scholar]
  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] [Google Scholar]
  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. [Google Scholar]
  22. T. Joachims, “Making large-scale support vector machine learning practical,” in Advances in kernel methods, ed: MIT Press, 1999, pp. 169–184. [Google Scholar]
  23. V. Kecman, Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models: MIT Press, 2001. [Google Scholar]

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