基于频域盲解卷积的机械设备状态监测与故障诊断  

Mechanical equipment condition monitoring and fault diagnosis based on frequency-domain blind deconvolution

作　　者：潘楠[1] 伍星[1] 迟毅林[1] 柳小勤[1] 刘畅[1] 

机构地区：[1]昆明理工大学机电工程学院,昆明650093

出　　处：《振动与冲击》2012年第12期34-41,共8页Journal of Vibration and Shock

基　　金：国家自然科学基金资助项目(50805071);云南省教育厅科学研究基金资助项目(2010Y380)

摘　　要：在频域盲解卷积(FDBD)模型的基础上,重点论述了其在工程信号特征提取中的关键技术:抑制循环—部分卷积误差的方法、次序不确定性的消除方法以及复数域盲分离算法的原理和应用。针对复杂环境或复杂机械设备结构中声、振信号的特征提取,全面综述了频域盲解卷积技术在机械设备状态监测和故障诊断中的研究现状,利用声学实验验证了其实际应用价值。最后指出了未来需要进一步研究的主要问题。Based on a model of frequency-domain blind deconvolution（FDBD）,key techniques in engineering signal feature extraction were comprehensively presented here including the methods of suppressing errors between cyclic and partial convolutions,the methods of removing permutation indeterminacy,the principle and application of complex-domain blind separation algorithms.Aiming at vibration and acoustic signal feature extraction in complex environment and equipments with complex mechanical structures,the application value of FDBD and its studying status in machinery condition monitoring and fault diagnosis were reviewed and summarized,its practical application value was verified by acoustics tests.Finally,the main problems to be studied further in this area were pointed out.

关 键 词：频域盲解卷积 机械故障诊断 循环—部分卷积误差 复数盲分离算法 次序不确定性 

分 类 号：TH165.3[机械工程—机械制造及自动化]