From Anomaly Detection to Novel Fault Discrimination for Wind Turbine Gearboxes With a Sparse Isolation Encoding Forest
Wenliao Du, Zhen Guo, Chuan Li, Xiaoyun Gong, Ziqiang Pu
Abstract
As vital renewable energy devices, wind turbines suffer from gearbox failures due to harsh speed increasing operations. Therefore, the gearbox fault diagnosis is crucial for wind turbine maintenance with reducing economic costs. However, obtaining faulty data is rather challenging, especially at the early fault stage. For this reason, a sparse isolation encoding forest (SIEF) is proposed aiming at both anomaly detection and novel fault discrimination for wind turbine gearboxes. In the present SIEF method, a sparse autoencoder is first trained with only normal data to obtain an optimized and robust weight structure. Newly acquired data corresponding to faulty or healthy conditions are sent to this encoder for feature extraction by encoding to its low dimensional space. All the data in low-dimensional space are fed to an isolation forest for anomaly detection and novel fault discrimination. In the addressed SIEF approach, only normal data are required to train the model for fault detection and further discrimination. It is consistent with the actual operations of the wind turbines, with much less dependence on the fault data for the model training. The proposed method was evaluated by fault diagnosis tests on the wind turbine gearboxes. Results show good performances of the proposal compared to peers.