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Using Neural Networks for Sensor ValidationThis paper presents the results of applying two different types of neural networks in two different approaches to the sensor validation problem. The first approach uses a functional approximation neural network as part of a nonlinear observer in a model-based approach to analytical redundancy. The second approach uses an auto-associative neural network to perform nonlinear principal component analysis on a set of redundant sensors to provide an estimate for a single failed sensor. The approaches are demonstrated using a nonlinear simulation of a turbofan engine. The fault detection and sensor estimation results are presented and the training of the auto-associative neural network to provide sensor estimates is discussed.
Document ID
19980209658
Acquisition Source
Legacy CDMS
Document Type
Technical Memorandum (TM)
Authors
Mattern, Duane L. (Scientific Monitoring, Inc. Tempe, AZ United States)
Jaw, Link C. (Scientific Monitoring, Inc. Tempe, AZ United States)
Guo, Ten-Huei (NASA Lewis Research Center Cleveland, OH United States)
Graham, Ronald (Allison Engine Co. Indianapolis, IN United States)
McCoy, William (Allison Engine Co. Indianapolis, IN United States)
Date Acquired
September 6, 2013
Publication Date
July 1, 1998
Subject Category
Aircraft Instrumentation
Report/Patent Number
AIAA Paper 98-3547
E-11258
NASA/TM-1998-208483
NAS 1.15:208483
Meeting Information
Meeting: Joint Propulsion Conference
Location: Cleveland, OH
Country: United States
Start Date: July 12, 1998
End Date: July 15, 1998
Sponsors: American Society of Mechanical Engineers, Society of Automotive Engineers, Inc., American Society for Electrical Engineers, American Inst. of Aeronautics and Astronautics
Funding Number(s)
PROJECT: RTOP 519-30-53
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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