Planetary gearboxes are more and more widely used in large and complex construction machinery such as those used in aviation, aerospace fields, and so on. However, the movement of the gear is a typical complex motion and is often under variable conditions in real environments, which may make vibration signals of planetary gearboxes nonlinear and nonstationary. It is more difficult and complex to achieve fault diagnosis than to fix the axis gearboxes effectively. A fault diagnosis method for planetary gearboxes based on improved complementary ensemble empirical mode decomposition (ICEEMD)-time-frequency information entropy and variable predictive model-based class discriminate (VPMCD) is proposed in this paper. First, the vibration signal of planetary gearboxes is decomposed into several intrinsic mode functions (IMFs) by using the ICEEMD algorithm, which is used to determine the noise component by using the magnitude of the entropy and to remove the noise components. Then, the time-frequency information entropy of intrinsic modal function under the new decomposition is calculated and regarded as the characteristic matrix. Finally, the fault mode is classified by the VPMCD method. The experimental results demonstrate that the method proposed in this paper can not only solve the fault diagnosis of planetary gearboxes under different operation conditions, but can also be used for fault diagnosis under variable operation conditions. Simultaneously, the proposed method is superior to the wavelet entropy method and variational mode decomposition (VMD)-time-frequency information entropy.

Wind energy is a widely utilized renewable energy source. Enhancing the reliability of wind turbines (WTs), especially the gearboxes, is crucial for efficient power generation. Real-time structural health monitoring (SHM) of the gearbox, including pre-start self-testing and ongoing checks, is essential for achieving optimal performance and safety. In this study, we developed a laboratory gear transmission platform simulating the WT spindle gearbox and integrated it with Ultrasonic Guided Wave (UGW) technology for active monitoring. Experiments were conducted to monitor the gearbox in three health states under five different working conditions. A modified health indicator (HI), MWF-PeEn, was extracted from UGW signals. However, significant spatial mapping variations of the HI matrix across different conditions hindered diagnostic accuracy. To address this issue, transfer learning techniques were applied to optimize the Extreme Learning Machine (ELM), improving the spatial distribution consistency of HI, and enhancing diagnostic accuracy. The precision and recall rate of the proposed FAA-ELM model are all above 90%. Compared to recent models, the FAA-ELM showed nearly a 10% improvement in precision, demonstrating its effectiveness and robustness in real-world scenarios. This underscores the practical advantages of SHM and the FAA-ELM for reliable WT maintenance and operation.