Adaptive Gated Attention Network With Weighted Metric Enhancement for Fault Diagnosis of Wind Turbine Gearbox

The vibration signal of the wind turbine gearbox (WTG) contains weak fault information due to its complex structure and noise interference, which hampers the performance of WTG fault diagnosis. To address this challenge, a novel deep learning (DL) approach is proposed, called adaptive gated attention network (AGANet) with weighted metric enhancement. The AGANet comprises an adaptive gated attention (AGA) mechanism that enhances the capability of fault feature extraction by selecting and weighing relevant feature channels from multiple perspectives. In addition, an adaptive weighted metric (AWM) framework is proposed, which can further strengthen the identifiability of fault features. By integrating these two innovations, the proposed AGANet can effectively extract distinguishable fault features from vibration signals and achieves outstanding performance in WTG fault diagnosis. The proposed AGANet is validated through extensive experiments using test-bench data as well as WTG data, and the results demonstrate the superior performance of AGANet-based WTG fault diagnosis.