Intelligent fault diagnosis of wind turbine gearbox based on Long short-term memory networks

Gearbox with complex structure is one of the most fragile components of wind turbines. Fault diagnosis of gearbox is crucial to reduce unexpected downtime and economic losses. This paper proposes an intelligent fault diagnosis method based on the Long Short-term Memory (LSTM) networks. Firstly, the multi- accelerometers vibration signals are divided into data segments. Then the common time domain features are extracted from these data segments. After that, these features are fed into the LSTM networks for fault pattern classification. The proposed method has no requirement for well-selected features, and also classifies the fault type accurately. The performance of the proposed method is validated by the multi- accelerometers vibration signals from wind turbine driven test rig. Through comparing with support vector machine (SVM) method, the superiority of the proposed method is verified. Moreover, the impact of different data segments on classification results is analyzed in this paper.