Noise-Robust Neural Network For Wind Turbine Gearbox Fault Diagnosis

The gearbox in a wind turbine is an important component, whereas it often operates in harsh environments, resulting in a relatively high failure rate. Fault diagnosis of wind turbine gearboxes by means of vibration signals is a feasible solution, but in practice, the vibration signals collected by sensors are often accompanied by noise, which affects the accuracy of fault diagnosis. In this paper, a noise-robust convolutional neural network (NRCNN) is utilized for fault diagnosis of wind turbine gearboxes with noisy vibration signals. The structure of multilayer convolutional layers and fully connected layers provides the NRCNN with excellent feature extraction capability, while the introduction of the dropout layer enables the NRCNN to have better generalization capability. Comprehensive experiments on two gearbox vibration datasets demonstrate that the NRCNN could accurately perform the diagnosis of vibration signals with noise.