Approximation Capability Analysis of Parallel Process Neural Network with Application to Aircraft Engine Health Condition Monitoring

Authors:
Gang Ding;Shisheng Zhong
Affiliations:
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, P.R. China;School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, P.R. China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

Parallel process neural network (PPNN) is a novel spatio-temporal artificial neural network. The approximation capability analysis is very important for the PPNN to enhance its adaptability to time series prediction. The approximation capability of the PPNN is analyzed in this paper, and it can be proved that the PPNN can approximate any continuous functional to any degree of accuracy. Finally, the PPNN is utilized to predict the iron concentration of the lubricating oil in the aircraft engine health condition monitoring to highlight the approximation capability of the PPNN, and the application test results also indicate that the PPNN can be used as a well predictive maintenance tool in the aircraft engine condition monitoring.