According to the World Health Organization,about 50 million people worldwide suffer from epilepsy.The detection and treatment of epilepsy face great challenges.Electroencephalogram(EEG)is a significant research object...According to the World Health Organization,about 50 million people worldwide suffer from epilepsy.The detection and treatment of epilepsy face great challenges.Electroencephalogram(EEG)is a significant research object widely used in diagnosis and treatment of epilepsy.In this paper,an adaptive feature learning model for EEG signals is proposed,which combines Huber loss function with adaptive weight penalty term.Firstly,each EEG signal is decomposed by intrinsic time-scale decomposition.Secondly,the statistical index values are calculated from the instantaneous amplitude and frequency of every component and fed into the proposed model.Finally,the discriminative features learned by the proposed model are used to detect seizures.Our main innovation is to consider a highly flexible penalization based on Huber loss function,which can set different weights according to the influence of different features on epilepsy detection.Besides,the new model can be solved by proximal alternating direction multiplier method,which can effectively ensure the convergence of the algorithm.The performance of the proposed method is evaluated on three public EEG datasets provided by the Bonn University,Childrens Hospital Boston-Massachusetts Institute of Technology,and Neurological and Sleep Center at Hauz Khas,New Delhi(New Delhi Epilepsy data).The recognition accuracy on these two datasets is 98%and 99.05%,respectively,indicating the application value of the new model.展开更多
针对滚动轴承早期故障信号具有周期性冲击的特点和被强噪声淹没而难以提取的问题,提出了一种基于固有时间尺度分解(Intrinsic Time Scale Decomposition,ITD)与稀疏编码收缩(Sparse Coding Shrinkage,SCS)集成的轴承故障特征提取方法(...针对滚动轴承早期故障信号具有周期性冲击的特点和被强噪声淹没而难以提取的问题,提出了一种基于固有时间尺度分解(Intrinsic Time Scale Decomposition,ITD)与稀疏编码收缩(Sparse Coding Shrinkage,SCS)集成的轴承故障特征提取方法(命名为ITD-SCS)。ITD能自适应地将振动信号分解成若干固有旋转分量(Proper Rotation,PR),选择有效的PR分量突显信号的冲击特征。进一步采用奇异值分解(Singular Value Decomposition,SVD)对每一有效PR实施滤噪作为SCS的前置滤噪单元以提高信号的稀疏性。最后,通过SCS利用极大似然估计方法提取合成信号中的冲击特征。将ITD-SCS应用于轴承内圈故障仿真信号和外圈实际故障振动信号的实验结果表明,ITD-SCS能有效提取强背景噪声下的轴承故障信号的冲击特征。展开更多
基于变量预测模型的模式识别(variable predictive model based class discriminate,VPMCD)方法是一种充分利用特征值之间相互内在关系进行多分类模式识别的新方法。对VPMCD算法进行了研究,并采用交叉验证法来选择VPMCD模型。针对机械...基于变量预测模型的模式识别(variable predictive model based class discriminate,VPMCD)方法是一种充分利用特征值之间相互内在关系进行多分类模式识别的新方法。对VPMCD算法进行了研究,并采用交叉验证法来选择VPMCD模型。针对机械故障振动信号的特征值之间的相互内在关系,结合本征时间尺度分解(intrinsic time-scale decom-position,ITD),提出了一种基于本征时间尺度分解和VPMCD的机械故障诊断方法。该方法首先利用ITD方法将原始信号分解若干个PR(proper rotation,PR)分量,然后提取第一个PR分量的无量纲时域统计参数组成特征向量,最后采用VPMCD方法进行机械故障诊断。通过滚动轴承故障诊断实验验证了该方法能有效地应用于小样本多分类机械故障诊断。展开更多
VPMCD(Variable Predictive Model Based Class Discriminate)是一种新的模式识别方法,它充分利用从原始数据中所提取的特征值之间的相互内在关系建立数学模型,从而进行模式识别。论文将VPMCD结合排列熵(Permutation Entropy,简称PE)方...VPMCD(Variable Predictive Model Based Class Discriminate)是一种新的模式识别方法,它充分利用从原始数据中所提取的特征值之间的相互内在关系建立数学模型,从而进行模式识别。论文将VPMCD结合排列熵(Permutation Entropy,简称PE)方法应用于滚动轴承故障诊断。首先采用ITD(Intrinsic Time-scale Decomposition,简称ITD)对滚动轴承振动信号进行分解,得到若干个固有旋转(Proper Rotation)分量,并对包含主要故障信息的PR分量提取排列熵作为故障特征值;然后,对VPMCD分类器进行训练;最后,采用VPMCD分类器进行故障识别和分类。实验数据的分析结果表明该方法能够有效地应用于滚动轴承故障诊断。展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.11701144,11971149)Henan Province Key and Promotion Special(Science and Technology)Project(Grant No.212102310305).
文摘According to the World Health Organization,about 50 million people worldwide suffer from epilepsy.The detection and treatment of epilepsy face great challenges.Electroencephalogram(EEG)is a significant research object widely used in diagnosis and treatment of epilepsy.In this paper,an adaptive feature learning model for EEG signals is proposed,which combines Huber loss function with adaptive weight penalty term.Firstly,each EEG signal is decomposed by intrinsic time-scale decomposition.Secondly,the statistical index values are calculated from the instantaneous amplitude and frequency of every component and fed into the proposed model.Finally,the discriminative features learned by the proposed model are used to detect seizures.Our main innovation is to consider a highly flexible penalization based on Huber loss function,which can set different weights according to the influence of different features on epilepsy detection.Besides,the new model can be solved by proximal alternating direction multiplier method,which can effectively ensure the convergence of the algorithm.The performance of the proposed method is evaluated on three public EEG datasets provided by the Bonn University,Childrens Hospital Boston-Massachusetts Institute of Technology,and Neurological and Sleep Center at Hauz Khas,New Delhi(New Delhi Epilepsy data).The recognition accuracy on these two datasets is 98%and 99.05%,respectively,indicating the application value of the new model.
文摘针对滚动轴承早期故障信号具有周期性冲击的特点和被强噪声淹没而难以提取的问题,提出了一种基于固有时间尺度分解(Intrinsic Time Scale Decomposition,ITD)与稀疏编码收缩(Sparse Coding Shrinkage,SCS)集成的轴承故障特征提取方法(命名为ITD-SCS)。ITD能自适应地将振动信号分解成若干固有旋转分量(Proper Rotation,PR),选择有效的PR分量突显信号的冲击特征。进一步采用奇异值分解(Singular Value Decomposition,SVD)对每一有效PR实施滤噪作为SCS的前置滤噪单元以提高信号的稀疏性。最后,通过SCS利用极大似然估计方法提取合成信号中的冲击特征。将ITD-SCS应用于轴承内圈故障仿真信号和外圈实际故障振动信号的实验结果表明,ITD-SCS能有效提取强背景噪声下的轴承故障信号的冲击特征。
文摘基于变量预测模型的模式识别(variable predictive model based class discriminate,VPMCD)方法是一种充分利用特征值之间相互内在关系进行多分类模式识别的新方法。对VPMCD算法进行了研究,并采用交叉验证法来选择VPMCD模型。针对机械故障振动信号的特征值之间的相互内在关系,结合本征时间尺度分解(intrinsic time-scale decom-position,ITD),提出了一种基于本征时间尺度分解和VPMCD的机械故障诊断方法。该方法首先利用ITD方法将原始信号分解若干个PR(proper rotation,PR)分量,然后提取第一个PR分量的无量纲时域统计参数组成特征向量,最后采用VPMCD方法进行机械故障诊断。通过滚动轴承故障诊断实验验证了该方法能有效地应用于小样本多分类机械故障诊断。
文摘VPMCD(Variable Predictive Model Based Class Discriminate)是一种新的模式识别方法,它充分利用从原始数据中所提取的特征值之间的相互内在关系建立数学模型,从而进行模式识别。论文将VPMCD结合排列熵(Permutation Entropy,简称PE)方法应用于滚动轴承故障诊断。首先采用ITD(Intrinsic Time-scale Decomposition,简称ITD)对滚动轴承振动信号进行分解,得到若干个固有旋转(Proper Rotation)分量,并对包含主要故障信息的PR分量提取排列熵作为故障特征值;然后,对VPMCD分类器进行训练;最后,采用VPMCD分类器进行故障识别和分类。实验数据的分析结果表明该方法能够有效地应用于滚动轴承故障诊断。
