为了实现对再制造电机转子质量的有效监控,采用超声波技术对其内部缺陷进行检测与评价。引入滤波效果良好的最优小波包滤波法和对超声波信号噪声含量变化敏感的Shannon信息熵算法,定义一种新的最优小波包Shannon熵(Best wavelet packet Shannon entropy,BWPSE)的概念,并提出基于BWPSE的超声波信号消噪方法。对采集到的再制造电机转子超声检测信号进行最优小波包滤波处理,得到各尺度的小波包分解系数,在此基础上计算各尺度小波包分解系数的Shannon熵,通过分析小波包系数Shannon熵的变化规律确定分解层数及阈值。采用该方法对再制造电机转子超声检测信号进行消噪处理,结果表明该方法对噪声消除比较彻底,对比Sqtwolog阈值小波分析及Heursure阈值小波分析等其他信号消噪方法,该方法可显著提高再制造电机转子内部缺陷定量分析的准确度。
In order to monitor the quality of remanufactured motor rotor effectively, ultrasonic technology is used for testing and evaluation of its internal flaws. Considering that best wavelet packet filter is good at filtering and shannon information entropy algorithm is sensitive to variation of noise content in ultrasonic signal, the two algorithms are integrated together, then a new conception named best wavelet packet Shannon entropy(BWPSE) is proposed, a new de-noising method based on BWPSE is proposed. The gathered ultrasonic testing signal of remanufactured motor rotor is processed by way of BWPSE, and the wavelet packet decomposition coefficients of each scale are gotten,which are employed to calculate the shannon entropy of each scale, by analyzing the variation of Shannon entropy,the decomposition level and threshold are determined. The proposed method are verified with the de-nosing result of remanufactured motor rotor, which prove that the BWPSE method is in favor of enhancing the accuracy of quantitative analysis for the flaw inside the remanufactured motor rotor , comparing with other de-nosing methods such as wavelet analysis based on Sqtwolog threshold and Heursure threshold, etc.
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