A novel information fusion method for the health monitoring of planetary gearbox based on veCMDE

Prognostic and health management of planetary gearbox can be of important to prevent catastrophic event and economic loss. The time-varying gear meshing position and the complex transmission path bring great challenge for the health monitoring of planetary gearbox. Recent researches show that the multichannel information fusion methods can provide more comprehensive fault information to achieve more accurate diagnostic result for the planetary gearbox. This paper proposed a novel information fusion method called variational embedded composite multiscale diversity entropy (veCMDE). The proposed veCMDE utilizes moving average windows under each scale factor to extract richer fault information at deep scales, which overcomes the defect of poor statistical reliability of the original variational embedding method. Based on veCMDE and eXtreme Gradient Boost classifier, a novel multichannel information fusion health monitoring frame of planetary gearbox has been proposed. Then, a rigid-flexible coupling dynamics model has been built to examine the performance and explore the dynamical properties of the proposed veCMDE. Lastly, an experiment using a practical planetary gearbox is conducted to prove the superiority of the proposed veCMDE method. Both simulation and experiment results demonstrate that the proposed veCMDE can enhance the statistical reliability at deep scales, resulting in a better performance in information fusion.