Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM

Renewable energy sources like wind energy are copiously available without any limitation. Reliability of wind turbine is critical to extract maximum amount of energy from the wind. The vibration signals in wind turbine's rotation parts are of universal non-Gasussian and nonstationarity and the fault samples are usually very limited. Aiming at these problems, this paper proposed a wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree Support Vector Machines (SVM). Firstly, the diagonal spectrum is calculated from vibration rotating machine as the input feature vector. Secondly, self-organizing feature map neural network is introduced to cluster the fault feature samples and construct a cluster binary tree. Then the multiple fault classifiers are designed to train and test samples. The wind turbine gear-box fault experiment results proved that this method can effectively extract features from nonstationary signals, and can obtain excellent results despite of less training samples.

► Wind turbine vibration signals are of universal non-Gasussian and nonstationarity. ► Diagonal spectrum is calculated from vibration rotating machine as the input feature vector. ► Multiple fault classifiers are designed to train and test the samples. ► Wind turbine gear-box fault test results prove the higher classification accuracy.