基于稳健独立分量分析的转子故障信息增强方法

针对实际转子振动信号中信源相互叠加干扰、故障信息微弱的问题,提出一种基于稳健独立分量分析(RICA)的转子故障信息增强方法。首先引入双树复小波变换,对信号进行降噪预处理,降低分离算法对噪声的敏感程度。再用稳健独立分量分析对降噪后信号进行分离和信息增强。并对比其他2种经典的盲源分离算法,通过数值仿真比较它们的分离效果。结果表明:新方法通过优化步长因子得到全局最优值,采用代数方法得到最优步长参数,实现简单,并且避免了预白化处理,使得算法运算量降低;对小数据量信号,算法收敛速度快、信号分离质量高。此方法可以更有效地分离故障源及提取信号的本质故障特征。

Method of rotor fault information enhancement based on robust independent component analysis

Considering the frequency aliasing and weakness of rotor vibration signal, a novel method of fault information enhancement based on robust independent component analysis (RICA) was presented. Firstly, the signals were denoised using dual-tree complex wavelet transform (DTCWT) to reduce the noise so as to improve the performance of RICA algorithm. Then the denoised signals were separated and information was enhanced by means of RICA. Compared with the other two classical blind source separation algorithms, the effectiveness of the proposed method was validated with the simulative signal. The results show that the global optimal value is given by optimizing the step factor using the method. The optimal step size parameters are gotten and prewhitening is avoided using algebraic methods with lower computational cost. The method shows high convergence speed and good source separation property, especially applicable for small-data records. The method can effectively separate fault signals and extract quantitative fault characteristics.