光谱学与光谱分析 |
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Artificial Neural Network Forecasting Method in Monitoring Technique by Spectrometric Oil Analysis |
YANG Yu-wei, CHEN Guo |
Civil Aviation College, Nanjing University of Aeronautics and Astronautics,Nanjing 210016, China |
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Abstract The spectrometric oil analysis (SOA) is an important technique for machine state monitoring and fault diagnosis, and forecasting machine state through SOA results has an advantage of finding out machine system wear fault early.Because Artificial Neural Network (ANN) possesses obvious advantages over traditional forecasting models for identifying non-linear model and forecasting non-even signal, the ANN forecasting approach was applied to monitoring technique by SOA, and the monitoring technique by SOA based on ANN forecasting was put forward.In the forecasting model, a 3-layer BP network structure was adopted.Aiming at the problem that ANN structure has a great effect on forecasting precision, the authors utilized the Genetic Algorithm (GA) to optimize the node number of input layer, the node number of hidden layer, and MSE (Mean of Squared Error) target value which was required for ANN training, and obtained the optimum forecasting model of ANN.Finally, the practical SOA data of some engine was analyzed and forecasted by ANN, and the forecasting result was compared with that of traditional ARMA model.The result fully shows the superiority and effectivity of the new method.
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Received: 2004-04-08
Accepted: 2004-07-21
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Corresponding Authors:
YANG Yu-wei
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Cite this article: |
YANG Yu-wei,CHEN Guo. Artificial Neural Network Forecasting Method in Monitoring Technique by Spectrometric Oil Analysis [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2005, 25(08): 1339-1343.
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URL: |
https://www.gpxygpfx.com/EN/Y2005/V25/I08/1339 |
[1] GAN Min-liang, ZUO Hong-fu, YANG Zhong, et al(干敏梁, 左洪福, 杨 忠,等).Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2000,20(1): 64. [2] Lapedes A Farber.Nonlinear Signal Processing using Neural Network: Prediction and System Modeling, Technical Report LA-UR-87-2662, Los Alamos National Laboratory.Los Alamos.NM, 1987. [3] Weigend A B,et al.International Journal of Neural System, 1990,(1): 193. [4] LIU Bao, HU Dai-ping(刘 豹, 胡代平).Journal of System Engineering(系统工程学报), 1999,14(4): 338. [5] Cholewo T, Zurada J M.Sequential Network Construction for Time Series Prediction.Proceedings of the IEEE International Joint Conference on Neural Networks, 1997,2034. [6] Lippmann R P.IEEE ASSP Magazine, 1987, April,12. [7] Cyberko G.Math.Control Signal System, 1989.34. [8] ZHANG Li-ming(张立明).Artificial Network Model and Its Application(人工神经网络的模型及其应用).Shanghai: Fudan University Press(上海: 复旦大学出版社), 1995.1. [9] Goldberg D.Genetic Algorithms in Search, Optimization and Machine Learning.Addison-Wesley, Reading, MA, 1989. [10] WANG Li, ZHUO Lin, HE Ying, et al(王 丽,卓 林,何 鹰,等).Spectroscopy and Spectral Analysis(光谱学与光谱分析),2004,24(12): 1537.
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