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.
出处
《振动与冲击》
EI
CSCD
北大核心
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)
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
frequency-domain blind deconvolution(FDBD)
mechanical fault diagnosis
error between cyclic and partial convolutions
complex-domain blind separation algorithm
permutation indeterminacy