A Symmetry-Aware Pitch Feature Encoder–Decoder Network for Condition Monitoring of Pitch Bearings in Wind Turbines

Pitch bearing is one of the critical components of the electric pitch system in wind turbines (WTs), and its early and reliable fault warning is of great significance to ensure the operational reliability and safety of the entire system. In this article, we develop a condition monitoring system for pitch bearing with available supervisory control data acquisition (SCADA) data. Motivated by the three-blade symmetrical structure of WTs, we propose a new symmetry-aware pitch feature encoder-decoder network named PitchNet. A group feature encoding strategy is first designed to extract intragroup variable-wise feature information. Then, a feature attention mechanism is applied to discover important intergroup features by dynamically assigning different weights to learned pitch representations. Finally, a feature decoder is used to reconstruct the pitch input features and derive the reconstruction residuals for early fault detection. The proposed PitchNet is evaluated using real cases of pitch-bearing cracks. Results have demonstrated that the proposed PitchNet presents better early fault warning ability and lower false alarm rates (FARs), which can provide promising and reliable monitoring results for wind farm operators.