基于工频电源驱动时扩展 Park 矢量方法诊断笼型异步电机转子断条故障的有效性,研究了在变频电源供电情况下该方法的诊断有效性。对断条故障电机变频电流进行了实测、分析和对比,结果表明:当采用变频电源供电时,传统的单相电流频谱分析...基于工频电源驱动时扩展 Park 矢量方法诊断笼型异步电机转子断条故障的有效性,研究了在变频电源供电情况下该方法的诊断有效性。对断条故障电机变频电流进行了实测、分析和对比,结果表明:当采用变频电源供电时,传统的单相电流频谱分析方法无法正确诊断转子断条故障,而扩展 Park 矢量方法仍然具有良好的诊断准确性和适用性。展开更多
To make the modulation classification system more suitable for signals in a wide range of signal to noise rate （SNR）, a feature extraction method based on signal wavelet packet transform modulus maxima matrix （WPT...To make the modulation classification system more suitable for signals in a wide range of signal to noise rate （SNR）, a feature extraction method based on signal wavelet packet transform modulus maxima matrix （WPTMMM） and a novel support vector machine fuzzy network （SVMFN） classifier is presented. The WPTMMM feature extraction method has less computational complexity, more stability, and has the preferable advantage of robust with the time parallel moving and white noise. Further, the SVMFN uses a new definition of fuzzy density that incorporates accuracy and uncertainty of the classifiers to improve recognition reliability to classify nine digital modulation types （i.e. 2ASK, 2FSK, 2PSK, 4ASK, 4FSK, 4PSK, 16QAM, MSK, and OQPSK）. Computer simulation shows that the proposed scheme has the advantages of high accuracy and reliability （success rates are over 98% when SNR is not lower than 0dB）, and it adapts to engineering applications.展开更多
文摘基于工频电源驱动时扩展 Park 矢量方法诊断笼型异步电机转子断条故障的有效性,研究了在变频电源供电情况下该方法的诊断有效性。对断条故障电机变频电流进行了实测、分析和对比,结果表明:当采用变频电源供电时,传统的单相电流频谱分析方法无法正确诊断转子断条故障,而扩展 Park 矢量方法仍然具有良好的诊断准确性和适用性。
文摘To make the modulation classification system more suitable for signals in a wide range of signal to noise rate （SNR）, a feature extraction method based on signal wavelet packet transform modulus maxima matrix （WPTMMM） and a novel support vector machine fuzzy network （SVMFN） classifier is presented. The WPTMMM feature extraction method has less computational complexity, more stability, and has the preferable advantage of robust with the time parallel moving and white noise. Further, the SVMFN uses a new definition of fuzzy density that incorporates accuracy and uncertainty of the classifiers to improve recognition reliability to classify nine digital modulation types （i.e. 2ASK, 2FSK, 2PSK, 4ASK, 4FSK, 4PSK, 16QAM, MSK, and OQPSK）. Computer simulation shows that the proposed scheme has the advantages of high accuracy and reliability （success rates are over 98% when SNR is not lower than 0dB）, and it adapts to engineering applications.
