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Proceedings Paper

Adaptive neural network/expert system that learns fault diagnosis for different structures
Author(s): Solomon Henry Simon
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Paper Abstract

Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

Paper Details

Date Published: 20 August 1992
PDF: 9 pages
Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992); doi: 10.1117/12.139947
Show Author Affiliations
Solomon Henry Simon, LTV Aerospace and Defense Co. (United States)

Published in SPIE Proceedings Vol. 1706:
Adaptive and Learning Systems
Firooz A. Sadjadi, Editor(s)

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