A neural network application to fault diagnosis for robotic manipulator

This paper illustrates a new approach of performing fault detection and isolation for robotic manipulators. The neural network fault isolation monitor utilizes a non-linear observer to generate a residual set. This residual set is presented to an artificial feedforward neural network with full connectivity. The neural network extracts specific characteristics which correlate to the operational mode of the system during the off-line training session. Once trained, the network performs efficiently in detecting and isolating faulty modes of the system. Although a robotic manipulator is used to illustrate the effectiveness of this approach, we believe that it can also be applied to other non-linear systems.

[1]  Alan S. Willsky,et al.  A survey of design methods for failure detection in dynamic systems , 1976, Autom..

[2]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[3]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[4]  Venkat Venkatasubramanian,et al.  A neural network methodology for process fault diagnosis , 1989 .

[5]  John J. Craig,et al.  Introduction to Robotics Mechanics and Control , 1986 .

[6]  Yann LeCun,et al.  Generalization and network design strategies , 1989 .

[7]  Frédéric Kratz,et al.  Fault detection in nonlinear systems , 1993, Proceedings of IEEE Systems Man and Cybernetics Conference - SMC.

[8]  Robert M. Farber,et al.  How Neural Nets Work , 1987, NIPS.

[9]  Timo Sorsa,et al.  Neural networks in process fault diagnosis , 1991, IEEE Trans. Syst. Man Cybern..

[10]  Ron J. Patton,et al.  Robust Model-Based Fault Diagnosis: The State of the ART , 1994 .

[11]  J.J. Gertler,et al.  Survey of model-based failure detection and isolation in complex plants , 1988, IEEE Control Systems Magazine.

[12]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..

[13]  R. Patton,et al.  Robust fault detection using eigenstructure assignment: a tutorial consideration and some new results , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[14]  Ron J. Patton,et al.  Robust fault diagnosis using the model-based approach , 1988 .