[1]冯驰,胡杨,王兆丰.基于分形理论的涡轮叶片特征提取[J].应用科技,2015,42(04):64-69.[doi:10.3969/j.issn.1009-671X.201411006]
 FENG Chi,HU Yang,WANG Zhaofeng.Feature extraction of turbine blades based on the fractal theory[J].Applied science and technology,2015,42(04):64-69.[doi:10.3969/j.issn.1009-671X.201411006]
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基于分形理论的涡轮叶片特征提取(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第42卷
期数:
2015年04期
页码:
64-69
栏目:
机电工程
出版日期:
2015-08-05

文章信息/Info

Title:
Feature extraction of turbine blades based on the fractal theory
作者:
冯驰1 胡杨1 王兆丰2
1. 哈尔滨工程大学 信息与通信工程学院, 黑龙江 哈尔滨 150001;
2. 西安航空发动机(集团)有限公司, 陕西 西安 710021
Author(s):
FENG Chi1 HU Yang1 WANG Zhaofeng2
1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
2. Xi’an Aero-engine (Group) Ltd., Xi’an 710021, China
关键词:
涡轮叶片特征提取分形理论K-meansReliefF
Keywords:
turbine bladesfeature extractionfractal theoryK-meansReliefF
分类号:
TK473
DOI:
10.3969/j.issn.1009-671X.201411006
文献标志码:
A
摘要:
采用了叶片温度这一叶片质量的重要指标,对数据预处理并进行特征提取,为涡轮叶片建立起特征模型。基于分形理论提取叶片温度信号的3种分形维数特征,结合K-means聚类分析和ReliefF算法计算各特征值的权重,从而建立起涡轮叶片的温度特征模型,实现对涡轮叶片故障的早期预警。统计结果表明,该特征模型能够较好地反映出处于故障状态的涡轮叶片的状态。
Abstract:
The data is preprocessed and features are extracted to establish a feature model of turbine blades by blade temperature, which is an important indicator of the turbine blade’s quality. Based on the fractal theory, the three fractal dimension features of blade temperature signals are extracted, the weight of each feature is calculated by combining with K-means clustering analysis and ReliefF algorithm, and thereby establish the temperature feature model of turbine blades, achieving early warning of the turbine blade failure. Statistical results show that the feature model can effectively reflect the state of turbine blades with failure.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014-11-14;改回日期:。
基金项目:黑龙江省自然科学基金资助项目(F201413).
作者简介:冯驰(1961-),男,教授;胡杨(1990-),女,硕士研究生.
通讯作者:胡杨,E-mail:huyang900218@163com
更新日期/Last Update: 2015-08-28