【摘要】 现场采样获得的电动机故障信号通常会含有很多的噪声,如果对噪声不能加以有效地消除,那么故障特征信息的提取将会受到很大的影响。该文通过对基于shannon熵的最优小波包基的快速搜索算法的探讨,提出了基于最优小波包基的电动机故障信号的消噪与检测方法。通过实例分析,验证了基于最优小波包基的信号消噪效果要优于小波方法或普通小波包方法的消噪效果。通过对消噪后的故障信号进行分析,提取出了故障特征信息。检测结果表明,在故障检测前先采用最优小波包基方法对故障信号进行消噪,有利于提高电动机故障检测的准确性。更多还原

【Abstract】 Faulted motor signal acquired at actual working spot usually includes large amount of noise. Extraction of the fault characteristic information will be influenced greatly if the faulted motor signal is not effectively denoised. Based ondiscussing the fast searching algorithm of best wavelet packet basis (BWPB)adoping shannon cntropy, a new method based on BWPB is presented to denoise and detect the faulted motor signal. It is demonstrated by analysis of actual signal example that adopting BWPB produces a better signal denoising effect compared with signal denoising adopting wavelet analysis or ordinary wavelet packet analysis. Analyzing is performed on the denoised signal and the fault charateristic information is extracted. The detection results show that signal denoisingapplying BWPB method is in favor of enhancing the detection accuracy of motor faults.更多还原