目的 提出一种基于画面分析的机械故障诊断方法， 用于印刷机械中印刷单元的故障模式分类。方法 通过印刷特定图像， 获取印刷单元在正常与故障状态下的印刷画面， 从网点覆盖率、 灰度特征和画面纹理特征等3个方面构建表征印刷单元状态的多元图像特征集， 并通过SVM构建故障识别网络。结果 利用多元统计方法分析了印刷单元6类故障与特征集的映射关系， 依靠画面特征实现了故障类别判断； 经过实际故障诊断试验验证， 所提出方法的准确率可达90%以上。结论 图像特征集对于印刷机故障有着很好的分类表征能力， 为印刷机维护提供了新的理论和方法。

A method of fault diagnosis based on image analysis was proposed for the classification of faults in printing unit of printing machine. By printing specific images, the printing images of the printing unit under both normal and abnormal states were obtained, and multiple feature sets of image characterizing the printing unit state were established from the aspects of the cover rate of printing dots, gray feature and texture feature. After that, a fault recognition net was built through SVM. The correlation between the image feature sets and six kinds of faults in the printing unit was analyzed with multivariate statistical methods. The faults were correctly classified based on the image features. Verification of actual fault diagnosis tests showed that the correct rate of the proposed method reached over 90% . Image feature sets had a high classification and characterization capacity for the faults of printing machine, and provided a new method and theory for the maintenance of printing machine.