摘要
针对恶劣工况下,混合故障信号特征弱化的现象,提出了一种基于EEMD-Hilbert信号重构和改进的循环谱相结合的轴承故障诊断方法.首先,利用二阶循环平稳理论,构造了系统调制频率下的二维循环谱相关密度函数,利用相呼应的调解信息规避干扰信息带来的误判;其次,设计了EEMD-Hilbert算法,并将该方法用于循环平稳信号重构,消除了高斯噪声与有色噪声干扰;最后,设计了轴承故障诊断工况模拟实验,实验分析表明该方法有效强化了弱循环平稳信号的特征,避免了故障信息的漏判.
Against the weakening of mixed-fault signal characteristics because of severe operating conditions, a bearing fault diagnosis method based on EEMD-Hilbert signal reconstruction and improved cyclic spectrum correlation algorithm is proposed. First, the cyclic spectrum function at the modulation frequency of the system is derived from the traditional second-order cyclostationary theory, this function can eliminate interference by its appropriate mediation information. Second, the EEMD-Hilbert algorithm is designed, and then is applied to the cyclostationary signal to eliminate the interference from gaussian noise and colored noise. At the last, a simulation experiment of bearing fault diagnosis conditions is designed, and the experimental results show that this method can effectively enhance the characteristics of the weak cycled stationary signal and avoid missed sentences.
作者
王小卉
隋广洲
弓满锋
WANG Xiao-hui;SUI Guang-zhou;GONG Man-feng(Institute of Electrical and Mechanical Engineering, Lingnan Normal University, Zhanjiang 524048, China)
出处
《控制工程》
CSCD
北大核心
2019年第7期1250-1255,共6页
Control Engineering of China
基金
国家自然科学基金青年基金资助项目(51705228)
广东省自然科学基金资助项目(S2013010012107)
岭南师范学院校级项目(LY1803)
