Fault Diagnosis of Turbine Based on Data-Driven

被引：1
|
作者：
Liao, Wei [1 ,2 ]
Li, Feng [1 ]
Han, Pu [2 ]
机构：
[1] Hebei Univ Engn, Handan 056038, Peoples R China
[2] North China Elect Power Univ, Beijing 071003, Peoples R China
来源：
ICICTA: 2009 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL II, PROCEEDINGS | 2009年
基金：
中国国家自然科学基金;
关键词：
data-driven; fault diagnosis; optimal presentative point; classification data; clustering;
D O I：
10.1109/ICICTA.2009.355
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
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.
引用
收藏
页码：499 / +
页数：2
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