空调热交换器性能异常检测技术是快速判断民机空调系统运行状态并合理安排维修任务的关键,传统的异常检测方法难以有效处理高维时序数据,无法实现系统早期故障预警。为此,本文提出了一种基于长短期记忆网络(LSTM,long-short term memory...空调热交换器性能异常检测技术是快速判断民机空调系统运行状态并合理安排维修任务的关键,传统的异常检测方法难以有效处理高维时序数据,无法实现系统早期故障预警。为此,本文提出了一种基于长短期记忆网络(LSTM,long-short term memory)与自编码器(AE,autoencoder)模型的无监督异常检测方法,用以识别民机空调系统异常运行状态。首先,基于民机空调系统原始传感器参数构建表征空调热交换器性能的特征监测参数;其次,构建LSTM-AE模型进行数据特征重构并计算重构误差;最后,使用孤立森林(iForest, isolation forest)进行无监督异常监测。将本文构建的无监督异常检测方法与传统方法对比,并建立模型评估指标,验证结果表明,所构建的模型方法可以对民机空调热交换器性能异常状态进行有效检测。展开更多
针对额外提取数据特征的方法需要花费大量时间和人力成本、轴承退化的线性预测精度低等问题,以及时序数据具有时间依赖关系的特点,提出了端到端的结合长短时记忆网络的深度变分自编码器模型(E2E Deep VAE-LSTM)用于轴承退化预测。通过改...针对额外提取数据特征的方法需要花费大量时间和人力成本、轴承退化的线性预测精度低等问题,以及时序数据具有时间依赖关系的特点,提出了端到端的结合长短时记忆网络的深度变分自编码器模型(E2E Deep VAE-LSTM)用于轴承退化预测。通过改进VAE的结构,并结合LSTM,该模型可以在含有异常值的数据集上直接进行训练和预测;使用系统重建误差表征轴承退化趋势,实现了轴承退化的非线性预测。在三个真实数据集上的实验结果表明,E2E Deep VAE-LSTM模型可以得到满意的预测结果,预测精度均高于现有的几种AE类模型及其他几种方法,且具有良好的泛化能力和抗过拟合能力。展开更多
Deep learning(DL)has shown its superior performance in dealing with various computer vision tasks in recent years.As a simple and effective DL model,autoencoder(AE)is popularly used to decompose hyperspectral images(H...Deep learning(DL)has shown its superior performance in dealing with various computer vision tasks in recent years.As a simple and effective DL model,autoencoder(AE)is popularly used to decompose hyperspectral images(HSIs)due to its powerful ability of feature extraction and data reconstruction.However,most existing AE-based unmixing algorithms usually ignore the spatial information of HSIs.To solve this problem,a hypergraph regularized deep autoencoder(HGAE)is proposed for unmixing.Firstly,the traditional AE architecture is specifically improved as an unsupervised unmixing framework.Secondly,hypergraph learning is employed to reformulate the loss function,which facilitates the expression of high-order similarity among locally neighboring pixels and promotes the consistency of their abundances.Moreover,L_(1/2)norm is further used to enhance abundances sparsity.Finally,the experiments on simulated data,real hyperspectral remote sensing images,and textile cloth images are used to verify that the proposed method can perform better than several state-of-the-art unmixing algorithms.展开更多
The manufacturing of nanomaterials by the electrospinning process requires accurate and meticulous inspection of related scanning electron microscope(SEM)images of the electrospun nanofiber,to ensure that no structura...The manufacturing of nanomaterials by the electrospinning process requires accurate and meticulous inspection of related scanning electron microscope(SEM)images of the electrospun nanofiber,to ensure that no structural defects are produced.The presence of anomalies prevents practical application of the electrospun nanofibrous material in nanotechnology.Hence,the automatic monitoring and quality control of nanomaterials is a relevant challenge in the context of Industry 4.0.In this paper,a novel automatic classification system for homogenous(anomaly-free)and non-homogenous(with defects)nanofibers is proposed.The inspection procedure aims at avoiding direct processing of the redundant full SEM image.Specifically,the image to be analyzed is first partitioned into subimages(nanopatches)that are then used as input to a hybrid unsupervised and supervised machine learning system.In the first step,an autoencoder(AE)is trained with unsupervised learning to generate a code representing the input image with a vector of relevant features.Next,a multilayer perceptron(MLP),trained with supervised learning,uses the extracted features to classify non-homogenous nanofiber(NH-NF)and homogenous nanofiber(H-NF)patches.The resulting novel AE-MLP system is shown to outperform other standard machine learning models and other recent state-of-the-art techniques,reporting accuracy rate up to92.5%.In addition,the proposed approach leads to model complexity reduction with respect to other deep learning strategies such as convolutional neural networks(CNN).The encouraging performance achieved in this benchmark study can stimulate the application of the proposed scheme in other challenging industrial manufacturing tasks.展开更多
目的自编码器作为一种无监督的特征提取算法,可以在无标签的条件下学习到样本的高阶、稠密特征。然而当训练集含噪声或异常时,会迫使自编码器学习这些异常样本的特征,导致性能下降。同时,自编码器应用于高光谱图像处理时,往往会忽略掉...目的自编码器作为一种无监督的特征提取算法,可以在无标签的条件下学习到样本的高阶、稠密特征。然而当训练集含噪声或异常时,会迫使自编码器学习这些异常样本的特征,导致性能下降。同时,自编码器应用于高光谱图像处理时,往往会忽略掉空域信息,进一步限制了自编码器的探测性能。针对上述问题,本文提出一种基于空域协同自编码器的高光谱异常检测算法。方法利用块图模型优良的背景抑制能力从空域角度筛选用于自编码器训练的背景样本集。自编码器采用经预筛选的训练样本集进行网络参数更新,在提升对背景样本表达能力的同时避免异常样本对探测性能的影响。为进一步将空域信息融入探测结果,利用块图模型得到的异常响应构建权重,起到突出目标并抑制背景的作用。结果实验在3组不同尺寸的高光谱数据集上与5种代表性的高光谱异常检测算法进行比较。本文方法在3组数据集上的AUC(area under the curve)值分别为0.9904、0.9888和0.9970,均高于其他算法。同时,对比了不同的训练集选择策略,与随机选取和使用全部样本进行对比。结果表明,本文基于空域响应的样本筛选方法相较对比方法具有较明显的优势。结论提出的基于空域协同自编码器的高光谱异常检测算法从空域角度筛选样本以提升自编码器区分异常与背景的能力,同时融合了光谱域和空域信息,进一步提升了异常检测性能。展开更多
文摘空调热交换器性能异常检测技术是快速判断民机空调系统运行状态并合理安排维修任务的关键,传统的异常检测方法难以有效处理高维时序数据,无法实现系统早期故障预警。为此,本文提出了一种基于长短期记忆网络(LSTM,long-short term memory)与自编码器(AE,autoencoder)模型的无监督异常检测方法,用以识别民机空调系统异常运行状态。首先,基于民机空调系统原始传感器参数构建表征空调热交换器性能的特征监测参数;其次,构建LSTM-AE模型进行数据特征重构并计算重构误差;最后,使用孤立森林(iForest, isolation forest)进行无监督异常监测。将本文构建的无监督异常检测方法与传统方法对比,并建立模型评估指标,验证结果表明,所构建的模型方法可以对民机空调热交换器性能异常状态进行有效检测。
文摘针对额外提取数据特征的方法需要花费大量时间和人力成本、轴承退化的线性预测精度低等问题,以及时序数据具有时间依赖关系的特点,提出了端到端的结合长短时记忆网络的深度变分自编码器模型(E2E Deep VAE-LSTM)用于轴承退化预测。通过改进VAE的结构,并结合LSTM,该模型可以在含有异常值的数据集上直接进行训练和预测;使用系统重建误差表征轴承退化趋势,实现了轴承退化的非线性预测。在三个真实数据集上的实验结果表明,E2E Deep VAE-LSTM模型可以得到满意的预测结果,预测精度均高于现有的几种AE类模型及其他几种方法,且具有良好的泛化能力和抗过拟合能力。
基金National Natural Science Foundation of China(No.62001098)Fundamental Research Funds for the Central Universities of Ministry of Education of China(No.2232020D-33)。
文摘Deep learning(DL)has shown its superior performance in dealing with various computer vision tasks in recent years.As a simple and effective DL model,autoencoder(AE)is popularly used to decompose hyperspectral images(HSIs)due to its powerful ability of feature extraction and data reconstruction.However,most existing AE-based unmixing algorithms usually ignore the spatial information of HSIs.To solve this problem,a hypergraph regularized deep autoencoder(HGAE)is proposed for unmixing.Firstly,the traditional AE architecture is specifically improved as an unsupervised unmixing framework.Secondly,hypergraph learning is employed to reformulate the loss function,which facilitates the expression of high-order similarity among locally neighboring pixels and promotes the consistency of their abundances.Moreover,L_(1/2)norm is further used to enhance abundances sparsity.Finally,the experiments on simulated data,real hyperspectral remote sensing images,and textile cloth images are used to verify that the proposed method can perform better than several state-of-the-art unmixing algorithms.
基金supported by the European Commission,the European Social Fund and the Calabria Region(C39B18000080002)supported by the UK Engineering and Physical Sciences Research Council(EPSRC)(EP/M026981/1,EP/T021063/1,EP/T024917/1)。
文摘The manufacturing of nanomaterials by the electrospinning process requires accurate and meticulous inspection of related scanning electron microscope(SEM)images of the electrospun nanofiber,to ensure that no structural defects are produced.The presence of anomalies prevents practical application of the electrospun nanofibrous material in nanotechnology.Hence,the automatic monitoring and quality control of nanomaterials is a relevant challenge in the context of Industry 4.0.In this paper,a novel automatic classification system for homogenous(anomaly-free)and non-homogenous(with defects)nanofibers is proposed.The inspection procedure aims at avoiding direct processing of the redundant full SEM image.Specifically,the image to be analyzed is first partitioned into subimages(nanopatches)that are then used as input to a hybrid unsupervised and supervised machine learning system.In the first step,an autoencoder(AE)is trained with unsupervised learning to generate a code representing the input image with a vector of relevant features.Next,a multilayer perceptron(MLP),trained with supervised learning,uses the extracted features to classify non-homogenous nanofiber(NH-NF)and homogenous nanofiber(H-NF)patches.The resulting novel AE-MLP system is shown to outperform other standard machine learning models and other recent state-of-the-art techniques,reporting accuracy rate up to92.5%.In addition,the proposed approach leads to model complexity reduction with respect to other deep learning strategies such as convolutional neural networks(CNN).The encouraging performance achieved in this benchmark study can stimulate the application of the proposed scheme in other challenging industrial manufacturing tasks.
文摘目的自编码器作为一种无监督的特征提取算法,可以在无标签的条件下学习到样本的高阶、稠密特征。然而当训练集含噪声或异常时,会迫使自编码器学习这些异常样本的特征,导致性能下降。同时,自编码器应用于高光谱图像处理时,往往会忽略掉空域信息,进一步限制了自编码器的探测性能。针对上述问题,本文提出一种基于空域协同自编码器的高光谱异常检测算法。方法利用块图模型优良的背景抑制能力从空域角度筛选用于自编码器训练的背景样本集。自编码器采用经预筛选的训练样本集进行网络参数更新,在提升对背景样本表达能力的同时避免异常样本对探测性能的影响。为进一步将空域信息融入探测结果,利用块图模型得到的异常响应构建权重,起到突出目标并抑制背景的作用。结果实验在3组不同尺寸的高光谱数据集上与5种代表性的高光谱异常检测算法进行比较。本文方法在3组数据集上的AUC(area under the curve)值分别为0.9904、0.9888和0.9970,均高于其他算法。同时,对比了不同的训练集选择策略,与随机选取和使用全部样本进行对比。结果表明,本文基于空域响应的样本筛选方法相较对比方法具有较明显的优势。结论提出的基于空域协同自编码器的高光谱异常检测算法从空域角度筛选样本以提升自编码器区分异常与背景的能力,同时融合了光谱域和空域信息,进一步提升了异常检测性能。
