Fault classification SOM and PCA for inertial sensor drift

FDI is an active research field in several areas. In fact, there are still many challenges in on-line detection and identification. Several approaches have been pursued such as model-based or knowledge-based techniques, however, these present several drawbacks like time consumption or the lack of ad...

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Bibliographic Details
Main Authors: Benitez-Perez, H., Garcia-Nocetti, F., Thompson, H.
Format: Conference Proceeding
Language:English
Subjects:
Aircraft
Automatic control
Control systems
Costs
Fault detection
Neural networks
Principal component analysis
Proposals
Self organizing feature maps
Systems engineering and theory
Online Access:Request full text
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Summary:FDI is an active research field in several areas. In fact, there are still many challenges in on-line detection and identification. Several approaches have been pursued such as model-based or knowledge-based techniques, however, these present several drawbacks like time consumption or the lack of adaptability. Here a proposal to classify faults for both known and unknown scenarios is presented. This is based upon a statistical approach, principal component analysis (PCA), and non-supervised neural networks such as self organizing maps (SOM). Experimental results are presented based upon an aircraft flight dynamics model.
DOI:10.1109/WISP.2005.1531654