一种改进的盲解卷积算法在轴承声学诊断中的应用

Application of an improved blind deconvolution algorithm to acoustic-based rolling bearing defect detection

针对时域盲解卷积算法滤波器长度估计困难的缺点,提出一种基于遗传算法优化的改进算法。该算法利用遗传算法搜索最佳时延,解决了盲解卷积结果不确定问题,并改进了信号分量的聚类指标,采用峭度作为独立分量间距离测度,提高了信号分量聚类的准确性,获得了可靠的估计信号。计算机仿真和实际环境中故障轴承声信号提取实验验证了该算法的有效性。

An improved time-domain blind deconvolution algorithm was proposed,based on genetic algorithm(GA) and higher order statistics(HOS).A newly defined distance measure based on kurtosis was employed to improve the classification accuracy of independent components in the cluster analysis process,and a GA was applied to search for an optimal length of blind deconvolution filters.With the help of these enhancements,the improved algorithm leads to perfect convolutive source separation for acoustic-based machine diagnosis.Both numerical and experimental studies were carried out.The results show that the algorithm can efficiently extract acoustic signals of fault bearings in real-world situations.