Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rollin...Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rolling bearing faults, a prognostic algorithm consisting of four phases was proposed. Since stacked denoising auto-encoder can be filtered, noise of large numbers of mechanical vibration signals was used for deep learning structure to extract the characteristics of the noise. Unsupervised pre-training method, which can greatly simplify the traditional manual extraction approach, was utilized to process the depth of the data automatically. Furthermore, the aggregation layer of stacked denoising auto-encoder(SDA) was proposed to get rid of gradient disappearance in deeper layers of network, mix superficial nodes’ expression with deeper layers, and avoid the insufficient express ability in deeper layers. Principal component analysis(PCA) was adopted to extract different features for classification. According to the experimental data of this method and from the comparison results, the proposed method of rolling bearing fault classification reached 97.02% of correct rate, suggesting a better performance than other algorithms.展开更多
Wireless sensor networks are increasingly used in sensitive event monitoring.However,various abnormal data generated by sensors greatly decrease the accuracy of the event detection.Although many methods have been prop...Wireless sensor networks are increasingly used in sensitive event monitoring.However,various abnormal data generated by sensors greatly decrease the accuracy of the event detection.Although many methods have been proposed to deal with the abnormal data,they generally detect and/or repair all abnormal data without further differentiate.Actually,besides the abnormal data caused by events,it is well known that sensor nodes prone to generate abnormal data due to factors such as sensor hardware drawbacks and random effects of external sources.Dealing with all abnormal data without differentiate will result in false detection or missed detection of the events.In this paper,we propose a data cleaning approach based on Stacked Denoising Autoencoders(SDAE)and multi-sensor collaborations.We detect all abnormal data by SDAE,then differentiate the abnormal data by multi-sensor collaborations.The abnormal data caused by events are unchanged,while the abnormal data caused by other factors are repaired.Real data based simulations show the efficiency of the proposed approach.展开更多
Icing is an important factor threatening aircraft flight safety.According to the requirements of airworthiness regulations,aircraft icing safety assessment is needed to be carried out based on the ice shapes formed un...Icing is an important factor threatening aircraft flight safety.According to the requirements of airworthiness regulations,aircraft icing safety assessment is needed to be carried out based on the ice shapes formed under different icing conditions.Due to the complexity of the icing process,the rapid assessment of ice shape remains an important challenge.In this paper,an efficient prediction model of aircraft icing is established based on the deep belief network(DBN)and the stacked auto-encoder(SAE),which are all deep neural networks.The detailed network structures are designed and then the networks are trained according to the samples obtained by the icing numerical computation.After that the model is applied on the ice shape evaluation of NACA0012 airfoil.The results show that the model can accurately capture the nonlinear behavior of aircraft icing and thus make an excellent ice shape prediction.The model provides an important tool for aircraft icing analysis.展开更多
In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and trans...In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and transformer models for global feature capture.The performance of Hformer was verified and evaluated based on the AAPM-Mayo Clinic LDCT Grand Challenge Dataset.Compared with the former representative state-of-the-art(SOTA)model designs under different architectures,Hformer achieved optimal metrics without requiring a large number of learning parameters,with metrics of33.4405 PSNR,8.6956 RMSE,and 0.9163 SSIM.The experiments demonstrated designed Hformer is a SOTA model for noise suppression,structure preservation,and lesion detection.展开更多
Increasing the resolution of seismic data has long been a major topic in seismic exploration.Due to the effect of high-frequency noises,traditional methods could only improve the resolution limitedly.To end this,this ...Increasing the resolution of seismic data has long been a major topic in seismic exploration.Due to the effect of high-frequency noises,traditional methods could only improve the resolution limitedly.To end this,this paper newly proposed a high-resolution seismic data processing method based on welleseismic combination after summarizing the research status on high resolution.Synthetic record and seismogram are similar in effective signals but dissimilar in noises.Their effective signals are regular and noises are irregular.And they are similar in adjacent frequency.Based on these“three-regularity”characteristics,the relationship between synthetic record and seismogram was established using the neural network algorithm.Then,the corresponding extrapolation algorithm was proposed based on the self-adaptive geological and geophysical variation of multi-layer network structure.And a model was established by virtue of this method and the theoretical simulation was carried out.In addition,it was tested from the aspects of frequency component and amplitude energy recovery,phase correction,regularity elimination and stochastic noise.And the following research results were obtained.First,this new method can extract high-frequency information as much as possible and remain middle and low-frequency effective information while eliminating the noises.Second,in this method,the idea of traditional methods to denoisefirst and then expand frequency is changed completely and the limitation of traditional methods is broken.It establishes the idea of expanding frequency and denoising simultaneously and increases the resolution to the uttermost.Third,this new method has been applied to a variety of reservoir descriptions and the high-resolution processing results have been improved significantly in precision and accuracy.展开更多
为了提升入侵检测的准确率,鉴于自编码器在学习特征方面的优势以及残差网络在构建深层模型方面的成熟应用,提出一种基于特征降维的改进残差网络入侵检测模型(improved residual network intrusion detection model based on feature dim...为了提升入侵检测的准确率,鉴于自编码器在学习特征方面的优势以及残差网络在构建深层模型方面的成熟应用,提出一种基于特征降维的改进残差网络入侵检测模型(improved residual network intrusion detection model based on feature dimensionality reduction,IRFD),进而缓解传统机器学习入侵检测模型的低准确率问题。IRFD采用堆叠降噪稀疏自编码器策略对数据进行降维,从而提取有效特征。利用卷积注意力机制对残差网络进行改进,构建能提取关键特征的分类网络,并利用两个典型的入侵检测数据集验证IRFD的检测性能。实验结果表明,IRFD在数据集UNSW-NB15和CICIDS 2017上的准确率均达到99%以上,且F1-score分别为99.5%和99.7%。与基线模型相比,提出的IRFD在准确率、精确率和F1-score性能上均有较大提升。展开更多
非侵入式负荷监测(NILM)通过分析电力总线数据估计单个负荷的功率波形,是电力系统能耗管理的关键技术之一。随着用户对设备能耗管理需求的增加,NILM的准确性成为研究的重点之一,但它容易受到功率类型、功率水平和负荷变化的影响。单一N...非侵入式负荷监测(NILM)通过分析电力总线数据估计单个负荷的功率波形,是电力系统能耗管理的关键技术之一。随着用户对设备能耗管理需求的增加,NILM的准确性成为研究的重点之一,但它容易受到功率类型、功率水平和负荷变化的影响。单一NILM模型面对不同类型的负荷时准确性差异较大,使用单一方法难以在各类负荷上均取得理想效果。因此,提出一种基于堆叠集成学习的非侵入式负荷高精度辨识方法 AMEL(Aggregation Method based on Ensemble Learning)。首先,选择在各种类型的负荷中表现最优的几种方法构建NILM模型库;其次,建立一个基于多层感知机(MLP)的NILM模型偏好框架,以实现对不同负荷的高精度监测。在UK-DALE数据集上的实验结果表明,与典型的NILM方法相比,所提方法的平均绝对误差(MAE)平均降低了35.6%,F1、召回率和马修斯相关系数(MCC)分别平均提升了33.5%、30.6%和32.1%。此外,通过比较现有的堆叠集成方法和各类设备的辨识波形,验证了所提方法的有效性。展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.51704138)
文摘Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rolling bearing faults, a prognostic algorithm consisting of four phases was proposed. Since stacked denoising auto-encoder can be filtered, noise of large numbers of mechanical vibration signals was used for deep learning structure to extract the characteristics of the noise. Unsupervised pre-training method, which can greatly simplify the traditional manual extraction approach, was utilized to process the depth of the data automatically. Furthermore, the aggregation layer of stacked denoising auto-encoder(SDA) was proposed to get rid of gradient disappearance in deeper layers of network, mix superficial nodes’ expression with deeper layers, and avoid the insufficient express ability in deeper layers. Principal component analysis(PCA) was adopted to extract different features for classification. According to the experimental data of this method and from the comparison results, the proposed method of rolling bearing fault classification reached 97.02% of correct rate, suggesting a better performance than other algorithms.
基金This work is supported by the National Natural Science Foundation of China(Grant No.61672282)the Basic Research Program of Jiangsu Province(Grant No.BK20161491).
文摘Wireless sensor networks are increasingly used in sensitive event monitoring.However,various abnormal data generated by sensors greatly decrease the accuracy of the event detection.Although many methods have been proposed to deal with the abnormal data,they generally detect and/or repair all abnormal data without further differentiate.Actually,besides the abnormal data caused by events,it is well known that sensor nodes prone to generate abnormal data due to factors such as sensor hardware drawbacks and random effects of external sources.Dealing with all abnormal data without differentiate will result in false detection or missed detection of the events.In this paper,we propose a data cleaning approach based on Stacked Denoising Autoencoders(SDAE)and multi-sensor collaborations.We detect all abnormal data by SDAE,then differentiate the abnormal data by multi-sensor collaborations.The abnormal data caused by events are unchanged,while the abnormal data caused by other factors are repaired.Real data based simulations show the efficiency of the proposed approach.
基金supported in part by the National Natural Science Foundation of China(No.51606213)the National Major Science and Technology Projects(No.J2019-III-0010-0054)。
文摘Icing is an important factor threatening aircraft flight safety.According to the requirements of airworthiness regulations,aircraft icing safety assessment is needed to be carried out based on the ice shapes formed under different icing conditions.Due to the complexity of the icing process,the rapid assessment of ice shape remains an important challenge.In this paper,an efficient prediction model of aircraft icing is established based on the deep belief network(DBN)and the stacked auto-encoder(SAE),which are all deep neural networks.The detailed network structures are designed and then the networks are trained according to the samples obtained by the icing numerical computation.After that the model is applied on the ice shape evaluation of NACA0012 airfoil.The results show that the model can accurately capture the nonlinear behavior of aircraft icing and thus make an excellent ice shape prediction.The model provides an important tool for aircraft icing analysis.
基金supported by the National Natural Science Foundation of China(Nos.11975292,12222512)the CAS"Light of West Chin"Program+1 种基金the CAS Pioneer Hundred Talent Programthe Guangdong Major Project of Basic and Applied Basic Research(No.2020B0301030008)。
文摘In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and transformer models for global feature capture.The performance of Hformer was verified and evaluated based on the AAPM-Mayo Clinic LDCT Grand Challenge Dataset.Compared with the former representative state-of-the-art(SOTA)model designs under different architectures,Hformer achieved optimal metrics without requiring a large number of learning parameters,with metrics of33.4405 PSNR,8.6956 RMSE,and 0.9163 SSIM.The experiments demonstrated designed Hformer is a SOTA model for noise suppression,structure preservation,and lesion detection.
文摘Increasing the resolution of seismic data has long been a major topic in seismic exploration.Due to the effect of high-frequency noises,traditional methods could only improve the resolution limitedly.To end this,this paper newly proposed a high-resolution seismic data processing method based on welleseismic combination after summarizing the research status on high resolution.Synthetic record and seismogram are similar in effective signals but dissimilar in noises.Their effective signals are regular and noises are irregular.And they are similar in adjacent frequency.Based on these“three-regularity”characteristics,the relationship between synthetic record and seismogram was established using the neural network algorithm.Then,the corresponding extrapolation algorithm was proposed based on the self-adaptive geological and geophysical variation of multi-layer network structure.And a model was established by virtue of this method and the theoretical simulation was carried out.In addition,it was tested from the aspects of frequency component and amplitude energy recovery,phase correction,regularity elimination and stochastic noise.And the following research results were obtained.First,this new method can extract high-frequency information as much as possible and remain middle and low-frequency effective information while eliminating the noises.Second,in this method,the idea of traditional methods to denoisefirst and then expand frequency is changed completely and the limitation of traditional methods is broken.It establishes the idea of expanding frequency and denoising simultaneously and increases the resolution to the uttermost.Third,this new method has been applied to a variety of reservoir descriptions and the high-resolution processing results have been improved significantly in precision and accuracy.
文摘为了提升入侵检测的准确率,鉴于自编码器在学习特征方面的优势以及残差网络在构建深层模型方面的成熟应用,提出一种基于特征降维的改进残差网络入侵检测模型(improved residual network intrusion detection model based on feature dimensionality reduction,IRFD),进而缓解传统机器学习入侵检测模型的低准确率问题。IRFD采用堆叠降噪稀疏自编码器策略对数据进行降维,从而提取有效特征。利用卷积注意力机制对残差网络进行改进,构建能提取关键特征的分类网络,并利用两个典型的入侵检测数据集验证IRFD的检测性能。实验结果表明,IRFD在数据集UNSW-NB15和CICIDS 2017上的准确率均达到99%以上,且F1-score分别为99.5%和99.7%。与基线模型相比,提出的IRFD在准确率、精确率和F1-score性能上均有较大提升。
文摘非侵入式负荷监测(NILM)通过分析电力总线数据估计单个负荷的功率波形,是电力系统能耗管理的关键技术之一。随着用户对设备能耗管理需求的增加,NILM的准确性成为研究的重点之一,但它容易受到功率类型、功率水平和负荷变化的影响。单一NILM模型面对不同类型的负荷时准确性差异较大,使用单一方法难以在各类负荷上均取得理想效果。因此,提出一种基于堆叠集成学习的非侵入式负荷高精度辨识方法 AMEL(Aggregation Method based on Ensemble Learning)。首先,选择在各种类型的负荷中表现最优的几种方法构建NILM模型库;其次,建立一个基于多层感知机(MLP)的NILM模型偏好框架,以实现对不同负荷的高精度监测。在UK-DALE数据集上的实验结果表明,与典型的NILM方法相比,所提方法的平均绝对误差(MAE)平均降低了35.6%,F1、召回率和马修斯相关系数(MCC)分别平均提升了33.5%、30.6%和32.1%。此外,通过比较现有的堆叠集成方法和各类设备的辨识波形,验证了所提方法的有效性。
