摘要
针对滚动轴承早期故障特征提取困难的问题,提出一种基于参数优化变分模态分解的轴承早期故障诊断方法。首先利用粒子群优化算法对变分模态分解算法的最佳影响参数组合进行搜索,搜索结束后根据所得结果设定变分模态分解算法的惩罚参数和分量个数,并利用参数优化变分模态分解算法对故障信号进行处理。原故障信号经过处理后被分解为若干本征模态函数分量,由此筛选出最佳信号分量并进行包络解调运算,最终通过分析信号的包络谱可判断轴承的故障类型。利用参数优化变分模态分解方法对轴承故障仿真和实测信号进行分析,均成功提取出微弱特征频率信息,表明参数优化变分模态分解方法可实现滚动轴承早期故障的有效判别,具有一定的可靠性和应用价值。
The fault feature of rolling bearing in early failure period is difficult to extract.An incipient fault diagnosis method for rolling bearing based on parameter optimized variational mode decomposition method was proposed.Particle swarm optimization algorithm was used to search for the best combination of influencing parameters of variational mode decomposition algorithm,the penalty parameter and number of components were then set according to the searching results,and the fault signal was processed by parameter optimized variational mode decomposition algorithm.The original fault signal was decomposed into several intrinsic mode function components.The best signal component was selected and processed by envelope demodulation algorithm,the fault type of bearing was judged by analyzing the envelope spectrum of the signal.The simulated and measured signals of fault bearing were analyzed by parameter optimized variational mode decomposition method and the weak characteristic frequency information was extracted successfully.The results show that the proposed method enables to judge the incipient fault of rolling bearing effectively with desired reliability.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2015年第5期73-81,共9页
Journal of Xi'an Jiaotong University
基金
河北省自然科学基金资助项目(E2014502052)
关键词
变分模态分解
粒子群算法
滚动轴承
早期故障诊断
variational mode decomposition
particle swarm algorithm
rolling bearing
incipientfault diagnosis
