Nonlinear neural network fault detection system and method

Details Nonlinear neural network fault detection system and method Nonlinear neural network fault detection system and method

2005-06-24

2010-02-09

Vincent, David R (Department: 2129)

Data processing: artificial intelligence

Neural network

Learning task

C706S027000

Reexamination Certificate

active

07660774

ABSTRACT:
A system and method for fault detection is provided. The fault detection system provides the ability to detect symptoms of fault in turbine engines and other mechanical systems that have nonlinear relationships. The fault detection system uses a neural network to perform a data representation and feature extraction where the extracted features are analogous to principal components derived in a principal component analysis. This neural network data representation analysis can then be used to determine the likelihood of a fault in the system.

REFERENCES:
Mark Kramer (“Nonlinear Principal Component Analysis Using autoassociative Neural Networks” 1991).
Menon et al (“Startup Fault Detection and Diagnosis in Turbine Engines” IEEE 2003).
Jonathon Shlens (“A tutorial on Principal Component Analysis” 2005).
Boaz Lerner et al., “A Comparative Study of Neural Network Based Feature Extraction Paradigms,” Pattern Recognition Letters, 1999, 7-14, vol. (20) 1. International Association of Pattern Recognition.
Boaz Lerner et al., “Feature extraction by Neural network Nonlinear Mapping for Pattern Classification.” The 13thInternational Conference on Pattern Recognition, 1996, 320-324, vol. 4, Vienna, Austria.
Jianchang Mao & Anil K. Jain, “Artificial Neural Networks for Feature Extraction and Multivariate Data Projection,” IEEE Transactions on Neural Network, Mar. 1995, 296-317, vol. 6 No. 2, Nicosia, Cyprus.
Mark A. Kramer, “Nonlinear Principal Component Analysis Using Autoasssociative Neural Networks,” AIChE Journal, Feb. 1991, 233-243, vol. 37 No. 2, American Institute of Chemical Engineers, Newark, DE.

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