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| Fault Detection of Wind Turbine Gearbox Based on Deep Autoencoder Network |
| Liu Huihai1, Zhao Xingyu2, Zhao Hongshan1, Song Peng3, Deng Chun3 |
1. School of Electrical and Electronic Engineering North China Electric Power University Baoding 071003 China;
2. School of Physics Science University of Chinese Academy of Sciences Beijing 110116 China;
3. State Grid Jibei Electric Power Research Institute Beijing 100045 China |
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Abstract In order to achieve the fault detection of wind turbine gearbox, a deep autoencoder network model from deep learning method based on supervisory control and data acquisition (SCADA) data and vibration signals of wind turbine gearbox is proposed in this paper. The deep autoencoder network, as one of the typical deep learning methods, can obtain the underlying rules and distribution characteristics of the data through learning features of original sample by layer-wise intelligent learning to form a more abstract and high-level representation. Firstly, restricted boltzmann machine was used to pre-train parameters and the back-propagation algorithm was used to optimize these parameters to build the deep autoencoder model in this paper. Then through encoding and decoding condition variables of gearbox, reconstruction error was computed as the gearbox condition monitoring variable. In order to monitor the trend change of reconstruction error effectively, the adaptive threshold was chosen as the decision criterion of gearbox fault. Finally, by utilizing the record data before and after fault to simulation, results showed the validity of deep autoencoder model on gearbox fault detection.
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Received: 04 November 2016
Published: 14 September 2017
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2017, Vol. 32 
