提出了一种基于最优分类特征的偏二叉树孪生支持向量机多分类算法（OCF-PBT-TWSVM），以实现针对齿廓图像边缘失真的非平稳瞬态随机信号进行有效分类，满足齿轮视觉测量实时性和失真补偿精度的要求.选择边缘动态分量信号最大值vm、边缘失真信号位置q_u、边缘失真率r_lv构成特征向量，组成训练样本集和测试样本集；以失真补偿需求为目标定义变权值特征向量测度γ，按照γ递减原则自顶向下完成OCF-PBT-TWSVM算法构建；采用粒子群优化方法进行算法参数优化，使c1,c2,g参数的性能达到最优.试验结果表明：在小样本数据情况下，提出的OCF-PBT-TWSVM多分类算法的最终分类准确率达96.96%，与PBT-SVM多分类算法相比具有更好的分类效果、训练速度也更快，能够满足后续失真补偿测量精度和齿轮视觉测量实时性的需求.

Proposed a partial binary tree twin support vector machine multi-classification algorithm based on optimal classification features （OCF-PBT-TWSVM） to achieve effective classification of non-stationary transient random signals with edge distortion of tooth profile images， and to meet the requirements of real-time gear vision measurement and distortion compensation accuracy Claim. Selected the maximum value vm of the edge dynamic component signal， the position of the edge distortion signal q_u， and the edge distortion rate r_lv to formed the feature vector，which constituted the training sample set and the test sample set at the same time； defined the variable weight feature vector measure γ with the target of distortion compensation， and completed the construction of the OCF-PBT-TWSVM algorithm according to γ decreasing； used the particle swarm optimization method to optimize the algorithm parameters to optimize the performance of the c1， c2， and g parameters. The test results show that， the final classification accuracy of the OCF-PBT-TWSVM multi-classification algorithm proposed in this paper is 96.96% in the case of small sample data， which has better classification effect and training speed than the PBT-SVM multi-classification algorithm. It is faster and can satisfy the requirements of subsequent distortion compensation measurement accuracy and real-time gear vision measurement.