A Hybrid Approach for Weak Fault Feature Extraction of Gearbox

Abstract

A novel hybrid fault diagnosis method based on ensemble empirical mode decomposition and weighted adaptive multi-scale morphological analysis (WAMMA) is proposed to detect the early damage of gearboxes. In this method, we propose a characteristic frequency ratio (CFR) method to determine the weighted coefficient for each scale of AMMA. First, multiple scales are obtained using the AMMA method. Second, the weighted coefficient of each scale in the AMMA method is calculated using the CFR. Third, the final results can be obtained by multiplying the weighted coefficients and filtering results with all scales. Since the performance of each scale of AMMA is evaluated using the CFR, the demodulation ability can be effectively improved. However, the WAMMA is easily disturbed by heavy noise when extracting early fault feature directly. A method combined EEMD with the WAMMA is proposed. The effectiveness of the proposed method has been verified using two experimental vibration signals of gearboxes. The results demonstrate that the proposed method has a superior performance in the extraction of weak fault characteristics of gearboxes in comparison with the WAMMA and EEMD-AMMA methods.

Keywords

• Ensemble empirical mode decomposition
• fault feature extraction
• weighted adaptive multi-scale morphological analysis
• early fault diagnosis gearbox