独立分量分析在机械振动信号分离中的应用

针对机械运转时工作环境复杂,监控采集的信号多为混合信号的情况,研究了如何从混合信号中分离出相对应的各振动源信号.采用独立分量分析的分离方法,通过对极大似然估计的目标函数进行简化,基于无需选择学习率的快速不动点迭代的优化方法,选用可同时估计亚高斯和超高斯独立成分的非线性函数估计概率密度,推导出了该算法.最后,对工程中几种特殊情况下的混合信号进行仿真和实验室实测的分离试验.结果表明,算法简单易行,具有快速稳定收敛的性质.

Application of Independent Component Analysis to Vibration Signal Separation of Rotational Machine

The observed vibration signal is always a mixture vibration signal because the operation condition is complex when the machine runs. This paper adopts the independent component analysis (ICA) to obtain the single signals from the mixture signal.The algorithm is performed by the fast fixed point iteration which need not choose the learning rate based on the implied target function of maximum likelihood estimation, and the subguassian and supe rguassian sources can be estimated at the same time by the chosen nonlinearity function applied to estimate the probability density function. The computer simu lations and the tests under especial conditions in engineering practice show that the algorithm is effective with simple, accuracy and faster convergence .