目的 为了实现印刷生产过程中网点异常状态的智能诊断，提出一种基于二维经验模式分解(BEMD)的网点特征提取方法。方法 通过对网点图像的BEMD分析，获取了其二维本征模式分量，并利用灰度共生矩阵(GLCM)对其进行特征提取，构建印刷网点的特征表示向量。结果 依托支持向量机决策方法开展分类实验，所提出的方法能够准确诊断出网点压力不当、水墨不均等异常状态，网点分类实验的正确率达到90%以上。结论 BIMF-GLCM分析对于网点特性有着很好的表征能力，相关研究为印刷网点智能诊断特征集的构建提供了有效方法。

The work aims to propose a dot feature extraction method based on the bi-dimensional empirical mode decomposition (BEMD), in order to achieve the intelligent diagnosis of the abnormal dot state in the printing process. Through the BEMD analysis on the dot image, its 2D intrinsic mode component was obtained and its feature extraction was done with the gray-level co-occurrence matrix (GLCM), so as to construct the feature representation of the printing dot. A classification experiment was carried out through the decision-making method of SVM. The proposed method could accurately diagnose such abnormal states as improper dot pressure and uneven ink. The accuracy rate of the dot classification experiment reached over 90%. BIMF-GLCM analysis has a good representational capacity for dot features and the related research provides an effective method for the construction of intelligent diagnosis feature set in the printing dots.