基于DAGSVM的高炉故障诊断研究

针对高炉故障诊断智能化程度低，对操作人员技术水平要求高等不足，提出了基于支持向量机的多类分类故障诊断方法．根据统计学原理，使用核函数将样本映射到高维空间进行训练．综合各种核函数的测试准确率，得到解决该问题的最佳核函数．通过比较不同的多类分类算法，提出了基于DAGSVM的诊断模型．实验结果表明该算法具有较高的识别准确率．

DAGSVM-Based Fault Diagnosis on Blast Furnace

Taking into consideration the low efficiency of applying intelligence to blast furnace fault diagnosis and the high demand to operator's technique,a multi-classification method based on support vector machine(SVM) is proposed.According to statistic learning theory,we use kernel functions to map the training samples into a high dimensional space for training.Combining the testing accuracy of different kernel functions,an optimal kernel function is obtained to solve this problem.By comparing different multi-calssification strategies,a diagnosis model based on DAGSVM(directed acyclic graph SVM) is constructed.Experiment results show that the proposed algorithm has a higher identification accuracy.