摘要
经验模态分解(EMD)是HHT变换处理非线性、非平稳信号的关键步骤。原始故障信号经过EMD分解为固有模态函数后进行希尔伯特变换,求出信号的瞬时频率来反映故障特征。但分解过程中产生的端点效应问题使得求出的瞬时频率失去准确性。文章在研究相似极值延拓算法的基础上提出了一种改进算法,增加了端点处是否作为极值点的判断,避免了端点数值超出包络线所造成的分析结果失真问题。仿真分析和实例故障诊断结果表明,文中算法计算效率高,能有效抑制经验模态分解过程中的端点飞翼现象。
Empirical mode decomposition （ EMD） is the key step of Hilbert-Huang transform in the process-ing non-linear and non-stationary signal. The original fault signal is decomposed into intrinsic mode function on Hilbert transform by Empirical mode decomposition, the instantaneous frequency of the signal to reflect the characteristics of the fault. But the end effects of EMD make the instantaneous frequency lost accuracy during the decomposition process. In this paper a improved method was developed based on the study of Similar extreme extension. The problem of the analysis distortion caused by the endpoint value beyond the envelope is avoided by increasing the judgment of whether the endpoint as the extreme point. Simulation a-nalysis and instance of fault diagnosis results show that the algorithm is of high efficiency and can effectively inhibit endpoint flying wing phenomenon in the process of empirical mode decomposition.
出处
《组合机床与自动化加工技术》
北大核心
2015年第9期78-80,85,共4页
Modular Machine Tool & Automatic Manufacturing Technique
基金
吉林省科技厅基金资助项目(20110303)
