摘要
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.*
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 muhi-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.
出处
《信息与控制》
CSCD
北大核心
2006年第5期619-623,共5页
Information and Control
基金
教育部流程工业自动化重点实验室开放基金资助项目
关键词
故障诊断
支持向量机
核函数
多类分类
高炉
fault diagnosis
support vector machine （SVM）
kernel function
multi-classification
blast furnace
