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26 May 2011 Echo state network prediction method and its application in flue gas turbine condition prediction
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Proceedings Volume 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering; 799735 (2011) https://doi.org/10.1117/12.887330
Event: Fourth International Seminar on Modern Cutting and Measuring Engineering, 2010, Beijing, China
Abstract
On the background of the complex production process of fluid catalytic cracking energy recovery system in large-scale petrochemical refineries, this paper introduced an improved echo state network (ESN) model prediction method which is used to address the condition trend prediction problem of the key power equipment--flue gas turbine. Singular value decomposition method was used to obtain the ESN output weight. Through selecting the appropriate parameters and discarding small singular value, this method overcame the defective solution problem in the prediction by using the linear regression algorithm, improved the prediction performance of echo state network, and gave the network prediction process. In order to solve the problem of noise contained in production data, the translation-invariant wavelet transform analysis method is combined to denoise the noisy time series before prediction. Condition trend prediction results show the effectiveness of the proposed method.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shaohong Wang, Tao Chen, and Xiaoli Xu "Echo state network prediction method and its application in flue gas turbine condition prediction", Proc. SPIE 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering, 799735 (26 May 2011); https://doi.org/10.1117/12.887330
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