个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:大连理工大学
学位:硕士
所在单位:控制科学与工程学院
办公地点:大连理工大学创新大厦(大黑楼)B612
联系方式:13500757605
电子邮箱:autowxl@dlut.edu.cn
Fault diagnosis of drilling process based on rough set and support vector machine
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论文类型:会议论文
发表时间:2013-05-19
收录刊物:EI、Scopus
卷号:709
页面范围:266-272
摘要:Drilling process is a complicated system with characteristics of uncertainty, fuzziness and time-varying. A new way of the fault diagnosis based on RS-SVM (Rough Set and Support Vector Machine) was proposed in this paper. The related engineering factors were reduced by Rough Set theory and the main factors of the drilling process were obtained. Then the Support Vector Machine was used to establish the diagnosis models, and then the problems that the traditional SVM cannot deal with dynamic data and are prone to dimension disasters with large samples were avoided. The application in Ha35 well, Liao He Oilfield indicates that the system can diagnose the type of faults quickly and accurately. So the method can be used to diagnose the drilling process. ? (2013) Trans Tech Publications, Switzerland.