基于小波包与SOM神经网络的截齿磨损状态识别

TD421.6; 为实现采煤机截割过程中截齿磨损状态的实时在线监测,采用声发射传感器对不同磨损程度截齿截割时的声发射信号进行采集,采用小波包分析方法分析声发射信号不同频带能量的变化规律,建立能量值的样本空间,构建基于SOM神经网络的截齿磨损识别模型,实现对截齿不同磨损状态的在线监测.通过随机测试实验对截齿磨损状态识别模型进行验证,结果表明,基于小波包分析与SOM神经网络的截齿预测磨损状态识别模型识别精度较高,测试样本识别精度约95％.研究结果为准确识别截齿的磨损状态、提高采煤机的工作效率提供一种重要的技术手段....

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Published in煤炭学报 Vol. 43; no. 7; pp. 2077 - 2083
Main Authors 张强, 顾颉颖, 刘峻铭, 刘志恒, 田莹
Format Journal Article
LanguageChinese
Published 辽宁工程技术大学机械工程学院,辽宁阜新,123000 01.07.2018
Subjects
截齿磨损
小波包分解
识别
采煤机
SOM神经网络
声发射信号
Online AccessGet full text
ISSN0253-9993
DOI10.13225/j.cnki.jccs.2017.1213

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Abstract TD421.6; 为实现采煤机截割过程中截齿磨损状态的实时在线监测,采用声发射传感器对不同磨损程度截齿截割时的声发射信号进行采集,采用小波包分析方法分析声发射信号不同频带能量的变化规律,建立能量值的样本空间,构建基于SOM神经网络的截齿磨损识别模型,实现对截齿不同磨损状态的在线监测.通过随机测试实验对截齿磨损状态识别模型进行验证,结果表明,基于小波包分析与SOM神经网络的截齿预测磨损状态识别模型识别精度较高,测试样本识别精度约95％.研究结果为准确识别截齿的磨损状态、提高采煤机的工作效率提供一种重要的技术手段.
AbstractList TD421.6; 为实现采煤机截割过程中截齿磨损状态的实时在线监测,采用声发射传感器对不同磨损程度截齿截割时的声发射信号进行采集,采用小波包分析方法分析声发射信号不同频带能量的变化规律,建立能量值的样本空间,构建基于SOM神经网络的截齿磨损识别模型,实现对截齿不同磨损状态的在线监测.通过随机测试实验对截齿磨损状态识别模型进行验证,结果表明,基于小波包分析与SOM神经网络的截齿预测磨损状态识别模型识别精度较高,测试样本识别精度约95％.研究结果为准确识别截齿的磨损状态、提高采煤机的工作效率提供一种重要的技术手段.
Author 张强
刘峻铭
刘志恒
顾颉颖
田莹
AuthorAffiliation 辽宁工程技术大学机械工程学院,辽宁阜新,123000
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Author_FL ZHANG Qiang
TIAN Ying
LIU Junming
GU Jieying
LIU Zhiheng
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Keywords 截齿磨损
小波包分解
识别
采煤机
SOM神经网络
声发射信号
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Snippet TD421.6; 为实现采煤机截割过程中截齿磨损状态的实时在线监测,采用声发射传感器对不同磨损程度截齿截割时的声发射信号进行采集,采用小波包分析方法分析声发射信号不同频带...
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Title 基于小波包与SOM神经网络的截齿磨损状态识别
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