An approach based on multi-scale ehirplet sparse signal decomposition is proposed to separate the malti-component polynomial phase signals, and estimate their instantaneous frequencies. In this paper, we have generate...An approach based on multi-scale ehirplet sparse signal decomposition is proposed to separate the malti-component polynomial phase signals, and estimate their instantaneous frequencies. In this paper, we have generated a family of multi-scale chirplet functions which provide good local correlations of chirps over shorter time interval. At every decomposition stage, we build the so-called family of chirplets and our idea is to use a structured algorithm which exploits information in the family to chain chirplets together adaptively as to form the polyncmial phase signal component whose correlation with the current residue signal is largest. Simultaueously, the polynomial instantaneous frequency is estimated by connecting the linear frequency of the chirplet functions adopted in the current separation. Simulation experiment demonstrated that this method can separate the camponents of the multi-component polynamial phase signals effectively even in the low signal-to-noise ratio condition, and estimate its instantaneous frequency accurately.展开更多
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...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。展开更多
基金supported by the National Science Foundation of China(No.50875078)
文摘An approach based on multi-scale ehirplet sparse signal decomposition is proposed to separate the malti-component polynomial phase signals, and estimate their instantaneous frequencies. In this paper, we have generated a family of multi-scale chirplet functions which provide good local correlations of chirps over shorter time interval. At every decomposition stage, we build the so-called family of chirplets and our idea is to use a structured algorithm which exploits information in the family to chain chirplets together adaptively as to form the polyncmial phase signal component whose correlation with the current residue signal is largest. Simultaueously, the polynomial instantaneous frequency is estimated by connecting the linear frequency of the chirplet functions adopted in the current separation. Simulation experiment demonstrated that this method can separate the camponents of the multi-component polynamial phase signals effectively even in the low signal-to-noise ratio condition, and estimate its instantaneous frequency accurately.
文摘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。
