Neural fault detection, isolation, and estimation design

Chi Yuan Chiang, Jyh-Chin Juang, Hussein M. Youssef

研究成果: Conference contribution

摘要

For autonomous and high performance systems, onboard fault detection, isolation, and estimation (FDIE) against sensor, actuator, and system failures are important features in ensuring performance and avoiding catastrophes. Existing FDIE designs can be roughly classified as trail-and-error. hardware redundancy, model-based analytic redundancy, or knowledge based expert system approaches. All are subject to, fully or partly, uncertainty, nonlinearity, and complexity, leading to high false alarm rate or erroneous classification. In this paper, a neural FDIE scheme is developed and verified using simulated data. The neural FDIE scheme has the following advantages: robustness against unmodeled dynamics; ability in handling nonlinear dynamics; flexibility in accounting for both discrete (jump) behavior and continuous degradation; modular and systematic design; and potential for unanticipated failures. The FDIE scheme is based on the general regression neural network (GRNN) concept, making the FDIE easy and fast to train. The design is verified using an F/A-18 system. A class of failure patterns are simulated. The performance and property of the neural FDIE scheme are assessed.

原文English
主出版物標題Proceedings of the International Joint Conference on Neural Networks
編輯 Anon
發行者Publ by IEEE
頁面1773-1776
頁數4
2
ISBN(列印)0780314212
出版狀態Published - 1993
事件Proceedings of 1993 International Joint Conference on Neural Networks. Part 2 (of 3) - Nagoya, Jpn
持續時間: 1993 十月 251993 十月 29

Other

OtherProceedings of 1993 International Joint Conference on Neural Networks. Part 2 (of 3)
城市Nagoya, Jpn
期間93-10-2593-10-29

All Science Journal Classification (ASJC) codes

  • Software

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