基于特征空间变换的纺纱过程多关联参数质量波动异常检测

TH165.4%TS111.9; 纺纱过程参数众多且相互关联,模糊了过程参数与纱线质量指标间的重要信息,针对纺纱过程参数强耦合带来的纺纱过程质量波动难以检测与控制的问题,提出一种基于特征空间变换的纺纱过程多关联参数质量波动异常检测方法.首先,分析纺纱过程参数间的关联关系,采用偏最小二乘法(PLS)消除参数之间的相关性,获得具有正交性的特征空间.然后,利用新的特征空间数据计算局部离群因子(LOF)统计量,判定纺纱过程是否稳定,定位异常波动区域.最后将相关性分析后的异常波动数据作为深度置信网络(DBN)模型的输入,识别纺纱过程异常波动类型.通过算例进行验证,结果显示所提模型将纺纱过程多关联参数异常...

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Published in计算机集成制造系统 Vol. 29; no. 7; pp. 2224 - 2232
Main Authors 胡胜, 李文, 赵小惠, 李哲, 陈臣
Format Journal Article
LanguageChinese
Published 西安工程大学 机电工程学院,陕西 西安 710048 31.07.2023
Subjects
local outlier factor
abnormal detection
特征空间变换
纺纱过程
spinning process
质量波动
deep belief network
quality fluctuation
fea-ture space transformation
深度置信网络
局部离群因子
异常检测
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Abstract TH165.4%TS111.9; 纺纱过程参数众多且相互关联,模糊了过程参数与纱线质量指标间的重要信息,针对纺纱过程参数强耦合带来的纺纱过程质量波动难以检测与控制的问题,提出一种基于特征空间变换的纺纱过程多关联参数质量波动异常检测方法.首先,分析纺纱过程参数间的关联关系,采用偏最小二乘法(PLS)消除参数之间的相关性,获得具有正交性的特征空间.然后,利用新的特征空间数据计算局部离群因子(LOF)统计量,判定纺纱过程是否稳定,定位异常波动区域.最后将相关性分析后的异常波动数据作为深度置信网络(DBN)模型的输入,识别纺纱过程异常波动类型.通过算例进行验证,结果显示所提模型将纺纱过程多关联参数异常检测精度提高到 98.2%.
AbstractList TH165.4%TS111.9; 纺纱过程参数众多且相互关联,模糊了过程参数与纱线质量指标间的重要信息,针对纺纱过程参数强耦合带来的纺纱过程质量波动难以检测与控制的问题,提出一种基于特征空间变换的纺纱过程多关联参数质量波动异常检测方法.首先,分析纺纱过程参数间的关联关系,采用偏最小二乘法(PLS)消除参数之间的相关性,获得具有正交性的特征空间.然后,利用新的特征空间数据计算局部离群因子(LOF)统计量,判定纺纱过程是否稳定,定位异常波动区域.最后将相关性分析后的异常波动数据作为深度置信网络(DBN)模型的输入,识别纺纱过程异常波动类型.通过算例进行验证,结果显示所提模型将纺纱过程多关联参数异常检测精度提高到 98.2%.
Author 赵小惠
李哲
陈臣
胡胜
李文
AuthorAffiliation 西安工程大学 机电工程学院,陕西 西安 710048
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Author_FL LI Zhe
CHEN Chen
LI Wen
HU Sheng
ZHAO Xiaohui
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Keywords local outlier factor
abnormal detection
特征空间变换
纺纱过程
spinning process
质量波动
deep belief network
quality fluctuation
fea-ture space transformation
深度置信网络
局部离群因子
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