Condition monitoring and fault diagnosis for wind turbine gearbox is significant to save operation and maintenance costs. However, strong interferences from high-speed parallel gears and background noises make fault detection of wind turbine planetary gearbox challenging. This paper addresses the fault diagnosis for wind turbine planetary ring gear, which is intractable for traditional spectral analysis techniques, since the fault characteristic frequency of planetary ring gear can be resulted from the revolving planet gears inducing modulations even in healthy conditions. The main contribution is to establish an adaptive empirical wavelet transform framework for fault-related mode extraction, which incorporates a novel meshing frequency modulation phenomenon to enhance the planetary gear related vibration components in wind turbine gearbox. Moreover, an adaptive Fourier spectrum segmentation scheme using iterative backward-forward search algorithm is developed to achieve adaptive empirical wavelet transform for fault-related mode extraction. Finally, fault features are identified from envelope spectrums of the extracted modes. The simulation and experimental results show the effectiveness of the proposed framework for fault diagnosis of wind turbine planetary ring gear. Comparative studies prove its superiority to reveal evident fault features and avoid the ambiguity from the planet carrier rotational frequency over ensemble empirical mode decomposition and spectral kurtosis. (C) 2018 Elsevier Ltd. All rights reserved.