摘要
提出了基于多尺度线调频基信号稀疏分解(Multi-scale Chirplet Sparse Signal Decomposition,简称MCSSD)的转子碰摩故障早期检测方法,该方法用MCSSD方法对转子碰摩故障振动信号进行单次分解,从原信号中分离出具有最大幅值的工频(或倍频)信号分量。由于MCSSD方法是采用线性直线逐段自适应逼近分析信号的各分量频率,分解得到的信号分量与真实信号分量具有很好的频率匹配特性,不会产生频率混叠现象,因此,与小波分解与EMD分解相比,MCSSD能更有效地从转子早期碰摩故障振动信号分离出最具最大幅值的工频(或倍频)信号分量。将该信号分量从原信号中去除,对残余信号分量做频谱分析,即可有效诊断转子早期碰摩故障。应用实例证明了该方法的有效性和优越性。
An approach based on multi-scale chirplet sparse signal decomposition is proposed for early detecting the rub-impact fault of the rotor systems. With the MCSSD method the power frequency component （or the multi-frequency component） with the maximum magnitude is extracted. In the MCSSD method the linear functions are adopted to fit the frequency curves of the signal components adaptively, so the decomposed component and the real signal component have good frequency matching characteristic, and the frequency-aliasing is avoided. Thereby comparing with the wavelet decomposition and the EMD decomposition, the MCSSD method can separate the power frequency component （or the multi-frequency component） from the rotor vibration signal more effectively. Through the spectrum analysis of the residual signal which is formed by subtracting this power frequency component （or the multi-frequency component） from the original signal, the early rub-impact default of the rotors can be detected sharply. The experiments in applications demostrate the validity and the superiority of the present method.
出处
《振动工程学报》
EI
CSCD
北大核心
2010年第2期220-224,共5页
Journal of Vibration Engineering
基金
国家高技术研究发展计划(863计划
2009AA04Z414)
国家自然科学基金项目(50875078)
高等学校博士学科点专项科研基金项目(20060532009)
教育部长江学者与创新团队发展计划项目(531105050037)
湖南大学汽车车身先进设计制造国家重点实验室自主课题(60870002)资助项目
关键词
多尺度线调频基
信号分解
碰摩故障
特征频率
故障检测
multi-scale chirplet base function
signal decomposition
rubrimpact fault
characteristic frequency
failure detecting
