摘要
针对发动机不同部位的机械故障特征容易混淆,且往往淹没在其他分量和强噪声中难于区分和提取的问题,提出了一种基于高阶累积量图像特征的柴油机故障诊断方法。截取柴油机6个工作循环的振动信号分别进行三阶累积量计算,累加平均得到1个工作循环信号的三阶累积量,提取柴油机不同故障状态基于三阶累积量图像灰度共生矩阵的图像纹理特征参数,利用支持向量机进行模式识别。试验结果表明:该方法既能抑制噪声干扰,又能充分利用高阶累积量图像中的纹理特征信息分析非稳态信号,提取的特征参数能有效识别发动机6种技术状态,与传统的基于高阶累积量的特征提取相比,提高了故障诊断准确率。
Different positions＇mechanical fault features of diesel engines are easy to be confused and they are often drowned in other components and color noises,so it is difficult to distinguish and extract them.Here,a fault diagnosis method based on high-order cumulant image features was proposed.Three-order cumulants for six cycles of vibration signals were calculated,respectively and the results were averaged to get three-order cumulant of one cycle.The image texture feature parameters based on three-order cumulant image gray level co-occurrence matrices （GLCM）.For different fault states of diesel engines were extracted.The pattern recognition was performed with a support vector machine （SVM）.The results showed that this method can inhibit noises and make full use of texture feature information of high-order cumulant image to analyze unsteady signals,the extracted features can be used to distinguish 6 technical states of diesel engines effectively,the fault diagnosis accuracy is improved compared with the traditional feature extraction based on high-order cumulant.
出处
《振动与冲击》
EI
CSCD
北大核心
2015年第11期133-138,共6页
Journal of Vibration and Shock
基金
总装备部预研项目(40407030302)
河北省自然基金资助(2013202256)
关键词
三阶累积量
图像纹理特征
灰度共生矩阵
特征提取
three-order cumulant
image texture feature
gray level co-occurrence matrix
feature extraction
作者简介
第一作者 沈虹 女，博士生，1982年生
