摘要
故障特征提取是滚动轴承故障诊断的关键环节。首先系统研究经验模式分解方法;然后介绍在经验模式分解基础上提出的几种方法,包括:希尔伯特-黄变换,局域均值分解以及集合经验模态分解。分析各种方法的基本原理、应用和特点。EMD与多种故障特征提取方法相结合是轴承故障特征提取的研究方向。
The ball-bearing fault feature extraction is a key problem in fault diagnosis. The empirical mode decomposition （EMD） method was systematically studied in this paper. Then, several methods for ball-bearing fault diagnosis based on EMD, including Hilbert-Huang transform （HHT）, the local mean decomposition （LMD） and the ensemble empirical mode decomposition （EEMD）, were introduced. The basic principles, features and applications of these methods were analyzed. It was indicated that combination of EMD with different fault feature extraction methods would become a new research focus for fault feature extraction of bearings.
出处
《噪声与振动控制》
CSCD
2013年第2期123-127,共5页
Noise and Vibration Control
基金
国家自然科学基金项目(基金编号:50975202)
