Fault Diagnosis of Turbine Based on Data-Driven

摘要：

A large number of real-time data and fault history data of turbine could be got through DCS, but the ability of data processing is lagging, a new method of fault diagnosis based on supervision of data-driven for turbine is introduced which is.The method of classification replace given data with points, using the weighted distance in place of Euclidean distance, establishing the iterative algorithm to search optimal representative point, the algorithm steps are given.According to the number of inconsistent samples points in different types of faults, the complexity relations of fault classification data is divided into the simple data, complex data.This paper points out a new algorithm of fault diagnosis which is based on representative points clustering; we could use the algorithm to analyze the turbine fault.

关键词： data-driven faultdiagnosis optimal presentative point classification data clustering

作者: Wei Liao Feng Li Pu Han

作者单位: Hebei University of Engineering, Handan, 056038, China North China Power Universities, Baoding, 0710 Hebei University of Engineering, Handan, 056038, China North China Power Universities, Baoding, 071003, China

会议类型: 国际会议

会议名称: 2009 Second International Conference on Intelligent Computation Technology and Automation(2009 第二届IEEE智能计算与自动化国际会议 ICICTA 2009)

会议地点: 长沙

会议语种:英文

页码: 1451-1454

在线出版日期: 2009-10-10（万方平台首次上网日期，不代表论文的发表时间）