针对冲击性故障信号的谱融合特征提取算法

Spectrum Fusion Feature Extraction Algorithm for Impulse Fault Signal

利用盲解卷积方法在时域中进行故障信号特征提取时,常会出现多个信号混淆分离结果,但以往的研究中只强调了分离的部分,而很少对分离后的信号进行进一步的处理,给实际应用造成不便。这里在盲解卷积和谱融合的基础之上,使用核改进的模糊c均值聚类算法,针对机械故障信号的脉冲特性,提出一种针对冲击性故障信号处理的实用型算法。计算机仿真实验证实了该算法的有效性。此算法优化了以往的聚类筛选方法,可以有效排除反卷积后诸多无用信号的干扰,将故障脉冲信号的特征准确提取出来,能提高故障诊断的效率。

When the blind deconvolution method is used to extract the feature of fault signal in time domain,often appear too many signal results to confuse separation results.However,in the previous research,only the separation part is emphasized,and the separated signal is rarely processed further,which makes the practical application inconvenient.On the basis of blind deconvo-lution and spectrum fusion,this paper uses the kernel improved fuzzy c-means clustering algorithm,aiming at the impulse char-acteristics of mechanical fault signal,and proposes a practical algorithm for impulse fault signal processing.The effectiveness of the algorithm is verified by computer simulation.This algorithm optimizes the previous clustering and screening methods,which can effectively eliminate the interference of many useless signals after deconvolution,extract the features of fault pulse signals ac-curately,and improve the efficiency of fault diagnosis.