Aiming at the problem that it is difficult to extract the features of planetary gearboxes under harsh conditions and difficult to classify accurately under various fault states. Based on the Empirical Wavelet Transform, the Improved Empirical Wavelet Transform is proposed, which replaces the original spectrum decomposition with the scale-spectrum decomposition which is more stable under noise interference. A fault diagnosis method combining Improved Empirical Wavelet Transform and Deep Extreme learning machine. Firstly, the signals of the planetary gearbox under different fault conditions are denoised by IEWT respectively and the FM-AM components of each order are extracted. Then, Multiscale sample entropy of the first six components with higher Envelope spectrum kurtosis was selected as the fault feature set and input into DELM for fault diagnosis and classification. The results of planetary gearbox fault diagnosis test show that compared with the fault diagnosis accuracy of EWT, EMD and DELM, the average fault recognition rate of this method can reach 97.6%, which has certain effectiveness.