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Using neural networks for sensor validation
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Graham, Ronald Jaw, Link C. Guo, Ten-Huei Mattern, Duane L. McCoy, William |
| Copyright Year | 1998 |
| Description | This 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. |
| File Size | 1026781 |
| Page Count | 14 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_19980209658 |
| Archival Resource Key | ark:/13960/t43r5sw5w |
| Language | English |
| Publisher Date | 1998-07-01 |
| Access Restriction | Open |
| Subject Keyword | Aircraft Instrumentation Component Reliability Neural Nets Redundancy Principal Components Analysis Reliability Engineering Real Time Operation Fault Tolerance Algorithms Fault Detection Simulation In-flight Monitoring Turbofan Engines Engine Monitoring Instruments Models Sensors Fail-safe Systems Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Technical Report |
