On-line fault detection using integrated neural networks

Jay Lee; John Tsai

doi:10.1117/12.140021

16 September 1992 On-line fault detection using integrated neural networks

Jay Lee, John Tsai

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.140021
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

A practical neural networks paradigm is described for material handling. The learning algorithm is a modification of the cerebella model articulation controller (CMAC) developed by Albus. A table look-up approach detects faults by monitoring the output patterns from sensors and actuators. By analyzing the timing sequence, abnormal conditions can be detected. CMAC offers an alternative to conventional back-propagated, multilayered networks, with the advantage of rapid convergence. The approach appears to be more efficient for the on-line and real-time applications required in automated systems.

Citation Download Citation

Jay Lee and John Tsai "On-line fault detection using integrated neural networks", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.140021

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