Gearbox is a critical transmission component in the drivetrain of wind turbine having a dominant failure rate and a highest downtime loss among all wind turbine subsystems. Ensemble empirical mode decomposition and principal component analysis have been extensively investigated for signal decomposition and fault feature extraction from the signals of a wind turbine gearbox. However, presence of background noise in wind turbine signals restricts the applicability of these algorithms in real scenarios. To solve this problem, a novel performance degradation assessment method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and kernel principal component analysis (KPCA) was proposed to de-noise and fuse vibration signals. A comparison is conducted between CEEMDAN-KPCA and other five cross combinations. After feature extraction, extreme learning machine (ELM) optimized by fruit fly of algorithm (FOA) is employed to predict the remaining use life (RUL) of wind turbine gearbox. To avoid the tedious hand-crafting hidden nodes, a dynamic adjustment strategy is presented for the improvement of search efficiency. The effectiveness of the proposed method is validated using simulated and experimental vibration signals. The results illustrate that the CEEMDAN-KPCA gets better result in signal de-noising. Compared with different swarm intelligence algorithms, the RUL prediction rates of wind turbine gearbox are improved by using the FOA-ELM prediction model with multiple parameters.