Evaluation of diagnosability of failure knowledge in manufacturing systems

The authors focus on the diagnostic step of the error recovery process in manufacturing systems and develop theories for evaluating the diagnosability of failure knowledge in manufacturing systems. The theories are based on the action-error-feature data set, described previously by the authors (1989). The definition of one-error diagnosability and the necessary and sufficient conditions for multiple-error diagnosability are given. A simple matrix operation, called covering analysis, for testing the conditions is also presented. The methodology provides a qualitative measure for the embedded diagnostic knowledge in terms of the ability to diagnose the number of multiple simultaneous errors in the system. This provides the diagnosability of the model and the feasibility of the computation, because the covering analysis is simple and at most two analyses are required to determine multiple-fault diagnosability. A discussion of the results obtained and simple examples to illustrate those results are presented.<<ETX>>

[1]  Michael J. Pazzani,et al.  Refining the Knowledge Base of a Diagnostic Expert System: An Application of Failure-Driven Learning , 1986, AAAI.

[2]  Michael J. Pazzani,et al.  Failure-Driven Learning of Fault Diagnosis Heuristics , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  Hidekatsu Tokumaru,et al.  A topological approach to failure diagnosis of large-scale systems , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  Ron Atkin,et al.  Mathematical structure in human affairs , 1976 .

[5]  Arthur C. Sanderson,et al.  AND/OR graph representation of assembly plans , 1986, IEEE Trans. Robotics Autom..

[6]  Rodney A. Brooks,et al.  Symbolic Error Analysis and Robot Planning , 1982 .

[7]  Shaw Jen Chang,et al.  The generation of diagnostic heuristics for automated error recovery in manufacturing workstations , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[8]  Bruce Randall Donald,et al.  Robot motion planning with uncertainty in the geometric models of the robot and environment: A formal framework for error detection and recovery , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[9]  Brian C. Williams,et al.  Reasoning about Multiple Faults , 1986, AAAI.

[10]  Maria L. Gini,et al.  Robot tracking and control issues in an intelligent error recovery system , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[11]  Frank DiCesare,et al.  A Causal Reasoning Approach For Planning Error Recovery In Automated Manufacturing Systems , 1989, Other Conferences.

[12]  Sankaran Srinivas.  Error recovery in robot systems. , 1977 .

[13]  Frank DiCesare,et al.  An algorithm for constructing a failure propagation tree in manufacturing systems , 1989, Proceedings. IEEE International Symposium on Intelligent Control 1989.

[14]  James A. Reggia,et al.  Diagnostic Expert Systems Based on a Set Covering Model , 1983, Int. J. Man Mach. Stud..

[15]  GERNOT METZE,et al.  On the Connection Assignment Problem of Diagnosable Systems , 1967, IEEE Trans. Electron. Comput..

[16]  Michael J. Pazzani,et al.  Explanation-Based Learning for Knowledge-Based Systems , 1987, Int. J. Man Mach. Stud..