摘要
以旋转机械振动三维参数图形为研究对象,提出了基于图形识别技术的旋转机械故障诊断方法。该方法用表征纹理的统计法、结构法及图形纹理方向的梯度法形成描述图形纹理特征的灰度-梯度-基元三维共生矩阵。该矩阵精确地反映了图形纹理的粗糙程度、重复方向和空间复杂度及纹理方向,准确地描述了图形灰度空间分布特性(概率)、空间统计相关性和图形内各像素点梯度的分布规律。描述了灰度统计和空间结构的纹理特征,有效地提取旋转机械状态参数图形中纹理特征信息。最后,利用RBF人工神经网络实现旋转机械故障诊断。在汽轮机转子试验台上进行了6种状态试验研究,诊断结果表明该文方法具有较高的诊断准确率,为旋转机械故障诊断探索了一条新路。
Taking a three-dimensional vibration image of rotating machinery as a studying object, the fault diagnosis method based on graphic recognition technology was investigated. This method used the statistical method, the structural method based on texture and the gradient method characterizing the graphic textural direction to form a matrix describing graphic textural characteristics. The matrix rough level, direction and spatial complex level and direction of texture. The graphic grayscale spatial distribution characteristic, spatial statistical dependence and pixel point gradient distribution rule were described accurately. Textural feature information in rotating mechanery state parameter graph was extracted effectively. Rotating machinery fault diagnosis could be conducted by using artificial neural networks after extracting the information of image texture characteristic. This method was validated to have higher diagnosis accuracy with 6 tests on a test table of a 600MW turbine.
出处
《振动与冲击》
EI
CSCD
北大核心
2012年第17期171-175,共5页
Journal of Vibration and Shock
基金
国家自然科学基金资助项目(50875056)
关键词
旋转机械
故障诊断
共生矩阵
图形
rotating machinery
fault diagnosis
co-occurrence matrix
graphic recognition
