A data enlargement strategy for fault classification through a convolutional auto-encoder

¹ Equipment Reliability, Prognostics and Health Management lab(ERPHM), School of Mechatronics Engineering, University of Electronic Science and Technology of China, 611731 Chengdu, China
² Chengdu Yute Technology Co., Ltd., 610000 Chengdu, China

^* Corresponding author: keshengwang@uestc.edu.cn

Abstract

The amount of data is of crucial to the accuracy of fault classification through machine learning techniques. In wind energy harvest industry, due to the shortage of faulty data obtained in real practice, together with ever changing operational conditions, fault detection and evaluation of wind turbine blade problems become intractable through conventional machine learning methods. In this paper, a modified unsupervised learning method, namely a convolutional auto-encoder based data enlargement strategy (ABE) is proposed for wind turbine blade fault classification. Limited simulation results for different levels of wind turbine icy blades are used for investigation. First, convolutional auto encoder is used to increase the amount of the data. Then, decision tree based xgboost tool, as an example, is used to demonstrate the effectiveness of data enlargement strategy for fault classification. The study shows that the proposed data enlargement strategy is an effective method to improve the fault classification accuracy through machine learning techniques.