|本期目录/Table of Contents|

[1]王江荣,文 晖,黄建华.基于差分进化算法的二次回归在矿井通风机故障诊断中的应用[J].工业仪表与自动化装置,2015,(01):50-53.
　WANG Jiangrong,WEN Hui,HUANG Jianhua.The two regression in ventilator fault diagnosis application based on difference evolutionary algorithm[J].Industrial Instrumentation & Automation,2015,(01):50-53.
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基于差分进化算法的二次回归在矿井通风机故障诊断中的应用(PDF)

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2015年01期
页码:
50-53
栏目:
出版日期:
2015-02-15

文章信息/Info

Title:
The two regression in ventilator fault diagnosis application based on difference evolutionary algorithm
文章编号:
20140110
作者:
王江荣; 文 晖; 黄建华
（兰州石化职业技术学院 信息处理与控制工程系，兰州730060）
Author(s):
WANG Jiangrong;  WEN Hui;  HUANG Jianhua
(Information Processing and Control Eengineering, Lanzhou Petrochemical College of Vocational Technology, Lanzhou 730060,China)
关键词:
矿井通风机; 故障诊断; 二次回归方程; 差分进化算法; 分类识别
Keywords:
mine ventilation machine;  fault diagnosis;  two regression equations;  differential evolution algorithm;  classification
分类号:
O235
DOI:
-
文献标志码:
A
摘要:
针对矿井通风机故障诊断过程中样本数据有限的特点，提出了一种经差分进化算法优化的二次回归诊断方法。将样本数据分为建模数据和测试数据，测试结果表明该方法具有适用性强、操作简单、精准度高，且无需太多样本数据等特点，值得推广。
Abstract:
according to the characteristics of the sample ventilator fault diagnosis process data is limited, put forward the differential evolution algorithm to optimize two regression diagnosis method. The sample data into the modeling data and test data, test results show that the method has strong applicability, simple operation, high accuracy, and does not need too many features such as sample data, worthy of promotion.

参考文献/References:

[1] 石瑶,任清阳.基于支持向量机的矿井通风机故障诊断系统的研究[J].自动化与仪器仪表,2013(5):18-20.[2] 刘朴,付胜.嵌入式矿井通风机在线监测与故障诊断系统设计[J].工矿自动化,2013,39(12):103-106.[3] 李扬,姚锦秀,汪仁煌.关联维计算及其在旋转机组振动故障征兆提取中的应用[J].机电工程技术,2002,31(6):51-53.[4] 汪光阳,周义莲.风机振动故障诊断综述[J].安徽工业大学学报,2006,23(1):64-68.[5] 刘金琨,沈晓蓉.系统辨识理论及MATLAB仿真[M].北京:电子工业出版社,2013:240-242.[6] 杨淑莹,张桦.群体智能与仿生计算 MATLAB技术实现[M].北京:电子工业出版社,2012:56-58.[7] 周润景,张丽娜.基于MATLAB与fuzzyTECH的模糊与神经网络设计[M].北京:电子工业出版社,2010:178-179.

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备注/Memo

备注/Memo:
-
更新日期/Last Update: 2015-02-15