A FAULT DIAGNOSIS STRATEGY USING LOCAL MODELS, FAULT INTENSITY AND BOUNDARY MODELS BASED ON SDG AND DATA-DRIVEN APPROACHES

摘要：

In this study, at first a hybrid local fault diagnostic model based on the signed digraph (SDG) which is a kind of model based approaches and a statistical learning model, support vector machine (SVM), would be proposed.And then, the fault intensity model and the fault boundary model were constructed for various fault intensities.Key aspects are the issue of resolving signatures resulting from the same fault but with differing intensities and making the decision tool to decide which a fault occurs.

关键词： Fault diagnosis Signed digraph Support vector machine Fault intensity Fault boundary

作者: CHANG JUN LEE GIBAEK LEE CHONGHUN HAN EN SUP YOON

作者单位: Department of Chemical and Biological Engineering, Seoul National University, Seoul, Korea Department of Chemical and Biological Engineering, Chungju National University, Chungju, Korea

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 2044-2048

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）