ESN-based Combination Method for Turbine Condition Prediction
- 10.2991/acaai-18.2018.5How to use a DOI?
- Turbine; health prediction; echo state network (ESN); wavelet analysis; principal components analysis (PCA)
As a nonlinear time series prediction method, echo state network (ESN) attracts more attention because of its good approximation capability for the nonlinear system. Aiming at the characters of nonlinear time series in the Turbine's condition prediction analysis, such as including noise and presenting chaos, etc. a combination method based on ESN was proposed. Firstly, the noise contained in nonlinear time series was reduced by the wavelet analysis. Then the training sample data were yielded via phase space reconstruction of the time series. After reducing the dimension of the training sample data by principal component analysis, all principal data were sent into the ESN prediction model. An actually dynamic pressure time series of aircraft power were conducted. The experiments compare the proposed method with traditional ESN prediction model on prediction accuracy and time cost. The results show that the proposed method can both effectively improve the prediction accuracy and efficiency, and it is an effective nonlinear time series prediction method in practice.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yanyan Liu AU - Yangming Guo AU - Aihua Wang PY - 2018/03 DA - 2018/03 TI - ESN-based Combination Method for Turbine Condition Prediction BT - Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018) PB - Atlantis Press SP - 17 EP - 22 SN - 1951-6851 UR - https://doi.org/10.2991/acaai-18.2018.5 DO - 10.2991/acaai-18.2018.5 ID - Liu2018/03 ER -