A Method for Extracting Fault Features Using Variable Multilevel Spectral Segmentation Framework and Harmonic Correlation Index
Kun Zhang, Haihong Tang, Peng Chen, Yonggang Xu, Aijun Hu
Abstract
Adaptive intelligent fault diagnosis technology can improve the automatic status monitoring capability and the success rate of fault diagnosis. This article proposed an adaptive mode decomposition and noise reduction algorithm (VAMD) that use a variable spectral segmentation framework to optimize analytical mode decomposition (AMD) to automatically decompose the mode information in rotating machinery signals. The framework relies on the variability of the window width and envelope estimation characteristics of the order statistics filter (OSF) to increase the diversity of the center frequencies and bandwidth. A novel harmonic correlation index (HCI) is designed to identify the characteristics of rotating machinery faults from various levels of results and improve the usability in mechanical equipment fault diagnosis. The proposed method has successfully achieved fault diagnosis of rolling bearing.