Acoustic Time Prediction of Bolt for Wind Power Tower Based on Deep Learning and Multi-source Data Fusion
A variety of data which affect the bolt state were integrated by the deep learning algorithm,and the acoustic time of bolt was predicted.Firstly,the tower vibration,inclination,ambient temperature and acoustic time information collected by the wind turbine monitoring system were preprocessed.Then the data were input into the CNN-BiLSTM model for training.Finally,the model was experimentally tested with monitoring data,and the comparison and verification were carried out with the LSTM model and the BiLSTM model.The results show that the CNN-BiLSTM has the higher prediction accuracy and can be used for early warning of bolt failure.
Wind TurbineTower Flange BoltsSound Time PredictionDeep Learning
万方数据
维普
