C.W. de Silva and K. Wong


  1. [1] K.K. Tan, S. Huang, T.H. Lee, A.S. Putra, C.S. Teo, & C.W. de Silva, Fault detection and diagnosis in mechatronic systems, in C.W. de Silva (Ed.), Mechatronic Systems: Devices, design, control, operation, and monitoring, (Boca Raton, FL: Taylor & Francis, CRC Press, 2008), Chapter 25.
  2. [2] G. Betta & A. Pietrosanto, Instrument fault detection and isolation: State of the art and new research trends, IEEE Transactions on Instrument and Measurement, 49(1), 2000, 100–107.
  3. [3] R. Iserman, Model based fault detection and diagnosis methods, Proc. American Control Conference, Seattle, Washington, 3, 1995, 1605–1603.
  4. [4] C.W. de Silva, Vibration – Fundamentals and practice, Second Edition (Boca Raton: CRC Press, 2007).
  5. [5] Q. Song, W.J. Hu, L. Yin, & Y.C. Soh, Robust adaptive dead zone technology for fault-tolerant control of robot manipulators using neural networks, Journal of Intelligent and Robotic Systems, 33, 2002, 113–137.
  6. [6] M. Goel, A.A. Maciejewski, & V. Balakrishnan, Analyzing unidentified locked-joint failures in kinematically redundant manipulators, Journal of Robotic Systems, 22, 2005, 15–29.
  7. [7] Y. Bar-Shalom, X.R. Li, & T. Kirubarajan, Estimation with applications to tracking and navigation (New York: WileyInterscience, 2001).
  8. [8] P. Zarchan & H. Musoff, Fundamentals of Kalman filtering: A practical approach (Reston: American Institute of Aeronautics and Astronautics, 2000).
  9. [9] M. Caron, V.J. Modi, S. Pradhan, C.W. de Silva, & A.K. Misra, Planar dynamics of flexible manipulators with slewing deployable links, Journal of Guidance, Control, and Dynamics, 21(4), 1998, 572–580.
  10. [10] T.W. Anderson, An introduction to multivariate statistical analysis (New York: John Wiley & Sons, 1984).
  11. [11] J.S. Meditch, Stochastic optimal linear estimation and control (New York: McGraw-Hill, 1969).Figure 8. A targeting and pointing manoeuvre of a twomodule manipulator in the presence of a locked-shoulder failure. of module 1 and then in an opposite direction to its final location at (0.568, 0.098). It is also seen that the tip path is composed of two curves, one before and the other after the shoulder joint is locked. From the simulation results, it is found that the final tip position is (0.809, 0.500) which almost corresponds to the target location (0.8, 0.5). 5. Conclusions This paper presented a technique for online identification of faults in an MDMS and for task execution in the presence of faults. The MDMS consist of a chain of modules, having a prismatic joint and a revolute joint in each module. A nonlinear model of the MDMS was developed, which was cast in a discrete-time state-space form for use in the failure identification method. Bayes hypothesis testing was employed in the failure identification scheme. First, a possible set of failure modes was defined, and a hypothesis was associated with each failure mode. In the developed method, the most likely hypothesis was selected depending on the observations of the manipulator response and a suitable test. The test minimized the maximum risk of accepting a false hypothesis, and accordingly the identification methodology was considered optimal. A bank of discrete Kalman filters was used for the computation of the hypothesis-conditioned information about the MDMS, as required in the decision logic. Through this approach, the MDMS, using kinematic redundancy and control, is able to satisfactorily execute a task even in the presence of a failure. Computer simulations of one-module and twomodule manipulators were carried out to demonstrate the effectiveness of the developed methodology for identification of three types of faults: sensor failure, locked joint, and free-wheeling joint; and for controlled manoeuvring in the presence of a locked joint. In particular, it was demonstrated that a two-module manipulator was able to 227

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