国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

Wind power as a green energy is playing an increasingly important role in China's energy structure. Rolling bearing as a main component of wind turbine drive system is one of the main defective parts. Timely detection of the faults of wind turbines’ rolling bearings is significant to the safe and stable operation of the wind farms. Aimed at the problem of gradient disappearing of the traditional recurrent neural network, the model is based on Long Short-Term Memory(LSTM) neural network to detect faults of wind turbines’ rolling bearings. First, to extract the feature vectors, wavelet packet transforming is used to process the vibration signals of wind turbines’ rolling bearings. Then the feature vectors are used as the input of LSTM neural networks, so as to diagnose the three common faults of wind turbines’ rolling bearings. Finally, through case studies, the validity of this method is verified. Even when the differences of faults’ features are not obvious, this method is still able to get good diagnostic results, which demonstrates the effectiveness of this method.