Neural fault detection, isolation, and estimation design

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Editors Anon
PublisherPubl by IEEE
Number of pages4
ISBN (Print)0780314212
Publication statusPublished - 1993
EventProceedings of 1993 International Joint Conference on Neural Networks. Part 2 (of 3) - Nagoya, Jpn
Duration: 1993 Oct 251993 Oct 29


OtherProceedings of 1993 International Joint Conference on Neural Networks. Part 2 (of 3)
CityNagoya, Jpn

All Science Journal Classification (ASJC) codes

  • Software


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