Icing Prediction of Fan Blade based on a Hybrid Model

doi:10.23940/ijpe.19.11.p6.28822890

Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (11): 2882-2890.doi: 10.23940/ijpe.19.11.p6.28822890

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Icing Prediction of Fan Blade based on a Hybrid Model

Cheng Peng^a,b, Jing He^a, Hao Chi^a, Xinpan Yuan^a,b, and Xiaojun Deng^a,*

^aSchool of Computer Science, Hunan University of Technology, Zhuzhou, 412007, China ;
^bSchool of Automation, Central South University, Changsha, 410083, China

Submitted on ; Revised on ; Accepted on
Contact: * E-mail address: chengpeng@csu.edu.cn
About author:Cheng Peng received his M.S. and PhD. degrees from the School of Information Science and Engineering at Central South University in 2010 and 2013, respectively. He is currently an associate professor. His current research interests include industry big data analysis and industry equipment health management.Jing He is a master's student in the School of Computer Science at Hunan University of Technology. Her main research interest is industry equipment health management.Hao Chi is a master's student in the School of Computer Science at Hunan University of Technology. His main research interest is industry equipment health management.Xinpan Yuan is an instructor in the School of Computer Science at Hunan University of Technology. His current research interests include industry big data analysis and industry equipment health management.Xiaojun Deng is a professor in the School of Computer Science at Hunan University of Technology. His current research interests include industry big data analysis and industry equipment health management.

Abstract

Abstract: For the problem that fan blade icing failures cannot be accurately predicted in advance, a data-driven fault prediction method is proposed in this paper. Firstly, the delay window is introduced to the PCA algorithm to extract the fault mode related features from the SCADA high-dimensional data. Then, the trained Elman neural network is adopted to predict the future value of the relevant features. Finally, a BP self-clustering algorithm is designed to predict the icing fault of the blade with the multi-source data fusion. The results show that the proposed method can effectively predict the icing failure of wind turbine blades and has reference significance for the maintenance of wind turbines.

Key words: wind turbine, fault prediction, blade icing, neural network, feature extraction, data-driven

Cheng Peng, Jing He, Hao Chi, Xinpan Yuan, and Xiaojun Deng. Icing Prediction of Fan Blade based on a Hybrid Model [J]. Int J Performability Eng, 2019, 15(11): 2882-2890.

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References

[1] M. Anuradha, B. K. Keshavan, T. S. Ramu,V. Sankar, “Probabilistic Modeling and Forecasting of Wing Power,” International Journal of Performability Engineering, Vol. 12, No. 4, pp. 353-368, 2016
[2] Z. Q. Zhai, C. Lin, H. M. Cui, H. Y. Liang,H. Y. Cheng, “Fatigue Life Prediction for Throwing Impeller of An Impeller Blower,” International Journal of Performability Engineering, Vol. 15, No. 1, pp. 127-137, 2019
[3] H. Y. Fan, P. K. Tarun, V. C. P.Chen, D. T. Shih, J. M. Rosenberger, S. B. Kim, et al., “Data-Driven Optimization for Dallas Fort Worth International Airport Deicing Activities,” Annals of Operations Research, Vol. 263, No. 1-2, pp. 361-384, April 2018
[4] Y. Zhao, J. M. Feng, Q. Zhou,X. Y. Peng, “Blade Fracture Analysis of a Motor Cooling Fan in a High-Speed Reciprocating Compressor Package,” Engineering Failure Analysis, Vol. 89, pp. 88-99, July 2018
[5] A. Alzghoul, M. Lfstrand,B. Backe, “Data Stream Forecasting for System Fault Prediction,” Computers and Industrial Engineering, Vol. 62, No. 4, pp. 972-978, May 2012
[6] A. Kusiak and A. Verma, “A Data-Mining Approach to Monitoring Wind Turbines,” IEEE Transactions on Sustainable Energy, Vol. 3, No. 1, pp. 150-157, 2012
[7] T. Zhang, “Design and Implementation of Wind Turbine Blade Condition Monitoring and Fault Diagnosis System,” Shanghai Institute of Electrical Engineering, 2016
[8] Q. Shi, “Model-based Detection for Ice on Wind Turbine Blades,” NTNU, 2017
[9] Y. H. Cao, J. M. Guo, Y. Li,H. Q. Lv, “Engine Life Prediction based on Degradation Data,” International Journal of Performability Engineering, Vol. 14, No. 12, pp. 2905-2914, 2018
[10] R. Chandra, “Competition and Collaboration in Cooperative Convolution of Elman Recurrent Neural Networks for Time-Series Prediction,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 26, No. 12, pp. 3123-3136, December 2015
[11] J. Bilski and J. Smoląg, “Parallel Architectures for Learning the RTRN and Elman Dynamic Neural Networks,” IEEE Transactions on Parallel and Distributed Systems, Vol. 26, No. 9, pp. 2561-2570, September 2015
[12] T. Y. Wu, C. Y. Zhang,Y. S. Zhao, “Monitoring and Warning Methods of Tailings Reservoir using BP Neural Network,” International Journal of Performability Engineering, Vol. 14, No. 6, pp. 1171-1180, June 2018
[13] Z. W. Shang, X. Liu, X. X. Liao, R. Geng, M. S. Gao,J. T. Yun, “Rolling Bearing Fault Diagnosis Method based on EEMD and GBDBN,” International Journal of Performability Engineering, Vol. 15, No. 1, pp. 230-240, 2019
[14] S. Jia, Q. Qiu, J. Li, Y. Li,Y. Cong, “BP Neural Network based Localization for a Front-Wheel Drive and Differential Steering Mobile Robot,” inProceedings of IEEE International Conference on Information and Automation, pp. 2270-2274, Lijiang, October 2015
[15] Y. Ge, L. Z. Guo,Y. Dou, “Remaining Useful Life Prediction of Machinery based on K-S Distance and LSTM Neural Network,” International Journal of Performability Engineering, Vol. 15, No. 3, pp. 895-901, 2019
[16] B. H. Zheng, “Material Procedure Quality Forecast based on Genetic BP Neural Network,” Modern Physics Letters, Vol. 31, No. 19-21, 2017
[17] H. H.Kim and N. R. Swanson, “Mining Big Data using Parsimonious Factor, Machine Learning, Variable Selection and Shrinkage Methods,” International Journal of Forecasting, Vol. 34, No. 2, pp. 339-354, 2018
[18] J. X. Bi, C. L. Zheng, H. Z. Huang, Y. Zhou,X. X. Li, “Load Analysis and Calculation Optimization of Horizontal Axis Wind Turbine Blades,” International Journal of Performability Engineering, Vol. 14, No. 12, pp. 3098-3108, December 2018
[19] Y. X. Zhang, X. D. Gao,S. Katayama, “Weld Appearance Prediction with BP Neural Network Improved by Genetic Algorithm During Disk Laser Welding,”Journal of Manufacturing Systems, Vol. 34, pp. 53-59, 2015
[20] L. Luo, R. J. Lovelett,B. A. Ogunnaike, “Hierarchical Monitoring of Industrial Processes for Fault Detection, Fault Grade Evaluation, and Fault Diagnosis,”AIChE Journal, Vol. 63, pp. 2781-2795, 2017

Icing Prediction of Fan Blade based on a Hybrid Model

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