Fault detection and isolation for robotic systems using a multilayer perceptron and a radial basis function network

Usually, fault detection and isolation schemes for robotic manipulators use the system mathematical model to generate the residual vector. However, modeling errors could obscure the faults and could be a false alarm source. In this paper a multilayer perceptron trained with backpropagation algorithm is employed to reproduce the robot input/output behavior generating the residual vector. Then, a radial basis function network is utilized to classify the residual vector generating the fault isolation. Three different algorithms have been employed to train this network. The first employs subset selection to choose the radial units from the training patterns. The second utilizes regularization to reduce the variance of the model. The third algorithm also uses regularization but, instead of one penalty term, each radial unit has an individual penalty term. Simulations employing a two-link manipulator are showed demonstrating that the system can detect and isolate correctly faults that occur in nontrained trajectories.

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

[2]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems: theory and application , 1989 .

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

[4]  Paul M. Frank,et al.  Observer-based supervision and fault detection in robots using nonlinear and fuzzy logic residual evaluation , 1996, IEEE Trans. Control. Syst. Technol..

[5]  M FrankPaul.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancya survey and some new results , 1990 .

[6]  Paul M. Frank,et al.  Fault Diagnosis in Dynamic Systems , 1993, Robotics, Mechatronics and Manufacturing Systems.

[7]  Joseph R. Cavallaro,et al.  Robotic fault detection and fault tolerance: A survey , 1994 .

[8]  Marios M. Polycarpou,et al.  Neural-network-based robust fault diagnosis in robotic systems , 1997, IEEE Trans. Neural Networks.

[9]  Y. C. Chen,et al.  A neural network application to fault diagnosis for robotic manipulator , 1996, Proceeding of the 1996 IEEE International Conference on Control Applications IEEE International Conference on Control Applications held together with IEEE International Symposium on Intelligent Contro.

[10]  Francis L. Merat,et al.  Introduction to robotics: Mechanics and control , 1987, IEEE J. Robotics Autom..

[11]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[12]  J. Mark.  Introduction to radial basis function networks , 1996 .

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

[14]  Mark J. L. Orr,et al.  Regularization in the Selection of Radial Basis Function Centers , 1995, Neural Computation.

[15]  Joseph R. Cavallaro,et al.  A dynamic fault tolerance framework for remote robots , 1995, IEEE Trans. Robotics Autom..

[16]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[17]  Janos Gertler,et al.  A Cautious Look at Robustness in Residual Generation , 1997 .

[18]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[19]  Mark J. L. Orr.  Local Smoothing of Radial Basis Function Networks , 1995 .

[20]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[21]  Paul M. Frank,et al.  Neural Networks in Model-Based Fault Diagnosis , 1996 .

[22]  Józef Korbicz,et al.  Neural Networks and their Application in Fault Detection and Diagnosis , 1997 .