Intelligent fault diagnosis of planetary gearbox based on refined composite hierarchical fuzzy entropy and random forest

Abstract

This paper presents a novel signal processing scheme by combining refined composite hierarchical fuzzy entropy (RCHFE) and random forest (RF) for fault diagnosis of planetary gearboxes. In this scheme, we propose a refined composite hierarchical analysis based method to improve the feature extraction performance of existing MFE and HFE methods. First, RCHFE is applied to extract the fault-induced information from the vibration signals. Because a refined composite analysis is used in HFF, the feature extraction capability of HFF can be effectively enhanced. Then, the extracted features are fed into the RF for effective fault pattern identification. The superiority of the proposed RCHFE–RF method is validated using both simulated and experimental signals. Results show that the proposed method outperforms MFE-RF and HFE-RF in identifying fault types of planetary gearboxes.