Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(635.42KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Distributed Cooperative Fault Diagnosis Method for Internal Components of Robot Systems
Hitoshi Kono, Musab Obaid Alhammadi, Yusuke Tamura, Atsushi Yamashita, Hajime Asama
Pages - 1 - 11     |    Revised - 01-03-2017     |    Published - 01-04-2017
Volume - 8   Issue - 1    |    Publication Date - April 2017  Table of Contents
MORE INFORMATION
KEYWORDS
Fault Detection, Distributed Cooperative System, Internal Component, Robot System.
ABSTRACT
Robot systems have recently been studied for real world situations such as space exploration, underwater inspection, and disaster response. In extreme environments, a robot system has a probability of failure. Therefore, considering fault tolerance is important for mission success. In this study, we proposed a distributed cooperative fault diagnosis method for internal components of robot systems. This method uses diagnostic devices called diagnosers to observe the state of an electrical component. These diagnosers execute each diagnosis independently and in parallel with one another, and it is assumed that they are interconnected through wireless communication. A fault diagnosis technique was proposed that involves gathering the diagnosis results. Further, computer simulations confirmed that the distributed cooperative fault diagnosis method could detect component faults in simplified fault situations.
CITED BY (0)  
1 Google Scholar
2 CiteSeerX
3 Scribd
4 slideshare
5 PdfSR
1 R.R. Murphy. "Trial by fire," IEEE Robotics & Automation Magazine. vol. 11, pp50-61, 2004.
2 K. Nagatani, S. Kiribayashi, Y. Okada, S. Tadokoro, T. Nishimura, T. Yoshida, E. Koyanagi and Y. Hada. "Redesign of rescue mobile robot Quince," in Proc. 2011 IEEE International Symposium on Security, and Rescue Robotics (SSRR), 2011, pp. 13-18.
3 J. Carlson and R.R. Murphy. "How UGVs physically fail in the field," IEEE Transactions on Robotics, vol. 21, pp. 423-437, 2005.
4 Z. Gao, C. Cecati and S.X. Ding. "A survey of fault diagnosis and fault-tolerant techniques-Part I: Fault diagnosis with model-based and signal-based approaches," IEEE Transactions on Industrial Electronics, vol. 62, pp. 3757-3767, 2015.
5 Z. Gao, C. Cecati and S.X. Ding. "A survey of fault diagnosis and fault tolerant techniques - Part II: Fault diagnosis with knowledge-based and hybrid/active approaches," IEEE Transactions on Industrial Electronics, vol. 62, pp. 3768-3774, 2015.
6 S. Okina, K. Kawabata, T. Fujii, Y. Kunii, H. Asama and I. Endo. "Self-diagnosis system of an autonomous mobile robot using sensory information," Journal of Robotics and Mechatronics, vol. 12, pp. 72-77, 2000.
7 I. Eski, S. Erkaya, S. Savas and S. Yildirim. "Fault detection on robot manipulators using artificial neural networks," Robotics and Computer-Integrated Manufacturing, vol. 27, pp. 115-123, 2011.
8 M. Schlechtingen and I.F. Santos. "Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection," Mechanical systems and signal processing, vol. 25, pp. 1849-1875, 2011.
9 D. Lefebvre. "On-line fault diagnosis with partially observed petri nets," IEEE Transactions on Automatic Control, vol. 59, no. 1919-1924, 2014.
10 Q. Liu, D. Zhu and S.X. Yang. "Unmanned underwater vehicles fault identification and fault-tolerant control method based on FCA-CMAC neural networks, applied on an actuated vehicle," Journal of Intelligent & Robotic Systems, vol. 66, pp. 463-475, 2012.
11 L.E. Parker. "ALLIANCE: An architecture for fault tolerant multirobot cooperation," IEEE Transactions on Robotics and Automation, vol. 14, pp. 220-240, 1998.
12 A.L. Christensen, R.O. Grady and M. Dorigo. "From fireflies to fault-tolerant swarms of robots," IEEE Transactions on Evolutionary Computation, vol. 13, pp. 754-766, 2009.
13 A.G. Millard, J. Timmis and A.F. Winfield. "Run-time detection of faults in autonomous mobile robots based on the comparison of simulated and real robot behavior," in Proc. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), 2014, pp. 3720-3725.
14 Z. Gao, J. Jia, J. Xie, W. D. Toh, P. Lin, H.Lyu, D. Julyanto, C.S. Chin and W.L. Woo, "Modelling and simulation of a 12-cell battery power system with fault control for underwater robot, " in Proc. of 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM) , 2015, pp.261-267.
15 RP. Bianchini Jr and RW. Buskens. "Implementation of online distributed system-level diagnosis theory," IEEE Transactions on Computers, vol. 41, pp.616-626, 1992.
16 S. Kelkar and R. Kamal. "Adaptive fault diagnosis algorithm for controller area network" IEEE Transactions on Industrial Electronics, vol. 61, pp. 5527-5537, 2014.
Dr. Hitoshi Kono
Department of Precision Engineering The University of Tokyo Tokyo 113-8656 - Japan
zin@ieee.org
Dr. Musab Obaid Alhammadi
Department of Electrical and Computer Engineering Khalifa University Abu Dhabi 127788 - United Arab Emirates
Dr. Yusuke Tamura
Department of Precision Engineering The University of Tokyo Tokyo 113-8656 - Japan
Dr. Atsushi Yamashita
Department of Precision Engineering The University of Tokyo Tokyo 113-8656 - Japan
Dr. Hajime Asama
Department of Precision Engineering The University of Tokyo Tokyo 113-8656 - Japan