From Anomaly Detection to Novel Fault Discrimination for Wind Turbine Gearboxes With a Sparse Isolation Encoding Forest

As vital renewable energy devices, wind turbines suffer from gearbox failures due to harsh speed increasing operations. Therefore, 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 (SAE) 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 (IF) 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 model training. The proposed method was evaluated by fault diagnosis tests on wind turbine gearboxes. Results show good performances of the proposal compared with peers.

I. Introduction

Increasing energy demands, limited fossil fuels, and increasingly severe environmental pollutions call for much attention to renewable energy. According to the existing studies, wind power is one of the most cost-effective renewable energy sources [1]. To convert wind energy into renewable electrical energy, wind turbines have attracted the world’s attention. However, it is expensive for the maintenance of wind turbines, which accounts for about 30% of its total expenses [2]. Therefore, it is necessary to adopt effective methods to reduce maintenance costs. Generally, fault diagnosis [3] is an effective way, whereas anomaly detection is an efficient strategy for the early-stage fault diagnosis developed rapidly in recent years.