【摘要】 针对气阀信号信噪比低、特征提取困难及故障样本较少、难以建立可靠的故障识别模型的问题,提出基于小波包特征熵支持向量机的气阀故障诊断方法。首先选择容错性较强的信息熵作为特征参数,通过对信号小波包分解,提取故障敏感频带的小波包特征熵作为输入向量,采用仅有的故障训练样本构建SVM分类器,建立气阀故障诊断模型。试验表明,该方法对小样本情形下气阀故障的非线性模式分类问题体现了良好的适应性,且具有特征提取工作量小的特点。更多还原

【Abstract】 The feature extraction and the failure identification model construction was extremely difficult due to the nonstationarity and nonlinearity of the compressor valve vibration signal and the little failure sample. According to the above characteristic, the information with good fault-tolerance ability was extracted as the feature parameter, which outlines the overall statistical characteristic of the signal. The extracted wavelet packet entropy was used as the input vector to construct the decision function and establish the valve wear failure SVM classifier based on the available failure sample. The experimental result demonstrates the effectiveness of the model in the nonstationarity signal feature extraction and nonlinearity pattern classification with small sample, and this model provides a practical valve wear failure diagnostic method.更多还原