Share Email Print
cover

Proceedings Paper

Application of hybrid wavelet neural network for missile fault diagnosis system
Author(s): Jun Hu; Guiyan Li; Shaowen Jia
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

In missile weapon system, exact fault prediction and diagnosis is very important for missile security, according to the specialty and complexity of the missile fault diagnosis, a novel expert system design method based on the hybrid neural network ensembles is proposed in this paper. To improve the limitation of applying traditional fault diagnosis method to the diagnosis method of the diagnosis of missile fault, with large amounts of typical missile fault samples and raw measurable parametric data available, the missile fault diagnosis system based on wavelet neural network ensembles can be created applying general construction techniques of the wavelet neural network fault diagnosis system, including signal binary wavelet transform, fault feature extraction/selection and network training. The back-propagation (BP) algorithm is used to fulfill the parameter initialization and the neural network structure (WNN). By means of choosing enough practical samples to verity the wavelet neural network (WNN) and the information representing the faults is inputted into the trained WNN, and according to the output result the type of fault can be determined. It's proved that through diagnosis of the missile from several different sides by use of different parameters the diagnosis result is more reliable. The method can be generalized to other devices' fault diagnosis.

Paper Details

Date Published: 10 November 2007
PDF: 5 pages
Proc. SPIE 6795, Second International Conference on Space Information Technology, 679564 (10 November 2007); doi: 10.1117/12.775216
Show Author Affiliations
Jun Hu, Naval Submarine Academy (China)
Guiyan Li, Naval Submarine Academy (China)
Shaowen Jia, Naval Aeronautical Engineering Academy (China)


Published in SPIE Proceedings Vol. 6795:
Second International Conference on Space Information Technology
Cheng Wang; Shan Zhong; Jiaolong Wei, Editor(s)

© SPIE. Terms of Use
Back to Top