Distributed Cooperative Fault Diagnosis Method for Internal Components of Robot Systems

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.

[1]  Wai Lok Woo,et al.  Modelling and simulation of a 12-cell battery power system with fault control for underwater robot , 2015, 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM).

[2]  Tim Murphy,et al.  Trial by Fire , 1995 .

[3]  Meik Schlechtingen,et al.  Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection , 2011 .

[4]  Seiga Kiribayashi,et al.  Redesign of rescue mobile robot Quince , 2011, 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics.

[5]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[6]  Jonathan Timmis,et al.  Run-time detection of faults in autonomous mobile robots based on the comparison of simulated and real robot behaviour , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Yasuharu Kunii,et al.  Self-diagnosis System of an Autonomous Mobile Robot Using Sensory Information , 2000, J. Robotics Mechatronics.

[8]  Sahin Yildirim,et al.  Fault detection on robot manipulators using artificial neural networks , 2011 .

[9]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches , 2015, IEEE Transactions on Industrial Electronics.

[10]  Supriya Kelkar,et al.  Adaptive Fault Diagnosis Algorithm for Controller Area Network , 2014, IEEE Transactions on Industrial Electronics.

[11]  Robin R. Murphy,et al.  How UGVs physically fail in the field , 2005, IEEE Transactions on Robotics.

[12]  Marco Dorigo,et al.  From Fireflies to Fault-Tolerant Swarms of Robots , 2009, IEEE Transactions on Evolutionary Computation.

[13]  Dimitri Lefebvre.  On-Line Fault Diagnosis With Partially Observed Petri Nets , 2014, IEEE Transactions on Automatic Control.

[14]  Lynne E. Parker,et al.  ALLIANCE: an architecture for fault tolerant multirobot cooperation , 1998, IEEE Trans. Robotics Autom..

[15]  Simon X. Yang,et al.  Unmanned Underwater Vehicles Fault Identification and Fault-Tolerant Control Method Based on FCA-CMAC Neural Networks, Applied on an Actuated Vehicle , 2012, J. Intell. Robotic Syst..

[16]  Implementation of On-Line Distributed System-Level Diagnosis Theory , 1992, IEEE Trans. Computers.