LFDNet: a lightweight fault diagnosis network for wind turbine gearboxes

Abstract With the fast growth of embedded and handheld intelligent devices, the demand for lightweight fault diagnosis (FD) models of wind turbine gearboxes (WTG) is increasing in actual industrial scenarios. The existing lightweight FD approaches suffer from poor diagnostic performance and low robustness in complex operating environments. In this paper, a FD approach is proposed based on a new lightweight FD network (LFDNet) for high diagnostic performance of WTG. In LFDNet, the stem module, stage module and downsample module are respectively redesigned to reduce the model complexity and maintain superior and robust diagnostic capabilities, which enable it to diagnose the faults of WTG precisely, even with noisy interference. In the validation experiments on a self-made dataset and an open-source dataset, the diagnostic accuracy achieves 99.41% and 99.69%, respectively, which is higher than multiple lightweight deep learning methods. In addition, the anti-noise experiment also shows that the proposed approach has good noise robustness.