检索结果-内蒙古大学图书馆

Health condition identification for rolling bearing using a multi-domain indicator-based optimized stacked denoising autoencoder

引用

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2020年第5期19卷 1602-1626页

作者： Yan, Xiaoan Liu, Ying Jia, Minping Nanjing Forestry Univ Sch Mechatron Engn Nanjing 210037 Jiangsu Peoples R China Southeast Univ Sch Mech Engn Nanjing Jiangsu Peoples R China

stacked denoising autoencoder is one of the most classic models of deep learning. However, there are two problems in the traditional stacked denoising autoencoder: (1) the parameter selection of stacked denoising autoencoder mainly depends on expert experience and (2) stacked denoising autoencoder is mainly restricted to learn automatically single-domain features from raw vibration signals while identifying the fault type, which implies that no linear mapping relationship located in other domains of vibration data is neglected, which may lead to the imperfect diagnostic results. Consequently, to address these issues, learn the well-rounded feature representation, and improve recognition accuracy, this article presents a novel approach called multi-domain indicator-based optimized stacked denoising autoencoder for automatic fault identification of rolling bearing. First, multi-domain indicator of the original vibration signal is constructed through calculating the expression of different domains (e.g. time frequency domain, and time frequency domain). Second, the constructed multi-domain indicator is regarded as the input dataset to train stacked denoising autoencoder architecture containing three hidden layers, and a recently reported nature-inspired algorithm named grasshopper optimization algorithm is employed to synchronously determine the model parameters of stacked denoising autoencoder, which is aimed at learning more robust and reliable feature representation. Finally, the feature representation learned in the testing set is fed into the trained stacked denoising autoencoder model containing softmax classifier for identifying bearing health conditions. The presented method is evaluated using two bearing vibration datasets. Experimental results indicate that our approach can provide high-accuracy recognition over 99% for bearing health condition, and it achieves more decent and precise classification results compared with some shallow learning model and standa

关键词： stacked denoising autoencoder multi-domain indicator grasshopper optimization algorithm rolling bearing health condition identification

来源：评论

学校读者我要写书评

暂无评论

denoising and feature extraction of weld seam profiles by stacked denoising autoencoder

引用

WELDING IN THE WORLD 2021年第9期65卷 1725-1733页

作者： Li, Ran Gao, Hongming Harbin Inst Technol State Key Lab Adv Welding & Joining West Straight St 92 Harbin 150001 Peoples R China

Active vision sensing is widely used in intelligent robotic welding for bead detection and tracking. Disturbed by welding noise such as arc light and spatter, it is a hard work to extract the laser stripe and feature values. This paper presents a method for denoising and feature extraction of weld seam profiles with strong welding noise in gas metal arc welding (GMAW) process by using stacked denoising autoencoder (SDAE). This algorithm encodes the images of various butt joints with strong welding noise to several useful intermediate representations, which can be decoded to the image of pure laser stripe in 1-pixel width. The results show little deviations when there are large spatters across the laser stripe. A back propagation neural network (BPNN) is developed to verify the reliability of the intermediate representations gotten from the encoder, in which the intermediate representations are input neurons and the weld seam width is output neuron. The average width error in training dataset and testing dataset is 0.042 mm and 0.061 mm. The results show that this algorithm can extract the weld seam profiles with strong welding noise and extract feature values accurately.

关键词： denoising Feature extraction Active vision sensing stacked denoising autoencoder Robotic welding

来源：评论

学校读者我要写书评

暂无评论

SDARE: A stacked denoising autoencoder method for game dynamics network structure reconstruction

引用

NEURAL NETWORKS 2020年 126卷 143-152页

作者： Huang, Keke Li, Shuo Dai, Penglin Wang, Zhen Yu, Zhaofei Cent South Univ Sch Automat Changsha 410083 Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Northwestern Polytech Univ Ctr Opt Imagery Anal & Learning Xian 710072 Peoples R China Peking Univ Dept Comp Sci & Technol Beijing 100871 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

Complex network is a general model to represent the interactions within technological, social, information, and biological interaction. Often, the direct detection of the interaction relationship is costly. Thus, network structure reconstruction, the inverse problem in complex networked systems, is of utmost importance for understanding many complex systems with unknown interaction structures. In addition, the data collected from real network system is often contaminated by noise, which makes the network structure inference task much more challenging. In this paper, we develop a new framework for the game dynamics network structure reconstruction based on deep learning method. In contrast to the compressive sensing methods that employ computationally complex convex/greedy algorithms to solve the network reconstruction task, we introduce a deep learning framework that can learn a structured representation from nodes data and efficiently reconstruct the game dynamics network structure with few observation data. Specifically, we propose the denoising autoencoders (DAEs) as the unsupervised feature learner to capture statistical dependencies between different nodes. Compared to the compressive sensing based method, the proposed method is a global network structure inference method, which can not only get the state-of-art performance, but also obtain the structure of network directly. Besides, the proposed method is robust to noise in the observation data. Moreover, the proposed method is also effective for the network which is not exactly sparse. Accordingly, the proposed method can extend to a wide scope of network reconstruction task in practice.

关键词： Complex network Network structure reconstruction Deep learning stacked denoising autoencoder Compressive sensing

来源：评论

学校读者我要写书评

暂无评论

Video surveillance image enhancement via a convolutional neural network and stacked denoising autoencoder

引用

NEURAL COMPUTING & APPLICATIONS 2022年第4期34卷 3079-3095页

作者： Che Aminudin, Muhamad Faris Suandi, Shahrel Azmin Univ Sains Malaysia Intelligent Biometr Grp Sch Elect & Elect Engn USM Engn Campus Nibong Tebal 14300 Pulau Pinang Malaysia

In an extensive-scale surveillance system, the quality of the surveillance camera installed varies. This variation of surveillance camera produces different image quality in terms of resolution, illumination, and noise. The quality of the captured image depends on the surveillance camera hardware, placement and orientation, and the surrounding light. A pixelated, low illumination and noisy image produced by a low-quality surveillance camera causes critical issues for video surveillance face recognition systems. To address these issues, a deep learning image enhancement (DLIE) model is proposed. By utilizing a deep learning architecture such as a convolutional neural network (CNN) and a denoising autoencoder, the image quality can be enhanced. The DLIE model is able to improve image resolution and illumination and reduce noise in an image. There are two deep learning blocks (DLB) in the DLIE model, which are DLB1 and DLB2. Both DLBs are arranged in parallel so that all the stated problems can be addressed simultaneously. DLB1 is proposed to address the occurrence of pixelated images by reconstructing a low-resolution image into a high-resolution image using a CNN. DLB2 used the capability of a denoising autoencoder to reconstruct the corrupted image into a clean image by enhancing the dark and noisy images. The output of each DLB is fused using image fusion to obtain the optimum image quality. The image is evaluated using the peak to signal noise ratio (PSNR) and structural similarity index (SSIM). The enhanced image from the DLIE model exhibits superior quality compared to the original image ranging from 13.3625 to 22.7728 for PSNR and 0.6207 to 0.8155 for SSIM.

关键词： Deep learning Convolutional neural network Super resolution Image enhancement stacked denoising autoencoder Image fusion Video surveillance camera

来源：评论

学校读者我要写书评

暂无评论

Enhanced stacked denoising autoencoder-Based Feature Learning for Recognition of Wafer Map Defects

引用

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2019年第4期32卷 613-624页

作者： Yu, Jianbo Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

In semiconductor manufacturing systems, defects on wafer maps tend to cluster and then these spatial patterns provide important process information for helping operators in finding out root-causes of abnormal processes. Promptly recognizing wafer map defects is an effective way to increase manufacturing process stability and then to improve yields. Deep learning has been widely applied and obtained many successes in image and visual analysis. This paper proposes an effective deep learning method, enhanced stacked denoising autoencoder (ESDAE) with manifold regularization for wafer map pattern recognition (WMPR) in manufacturing processes. This study will concentrate on developing a deep learning model to learn effective discriminative features from wafer maps through a deep network architecture for WMPR improvement. An indication based on ESDAE is developed for detecting map defects online. An ESDAE-based classifier is finally developed to implement recognition of wafer map defects. The most motivation for developing deep learning and manifold regularization techniques is to achieve higher accuracy and applicability than that of some regular recognizers. The effectiveness of the proposed method has been demonstrated by experimental results from a real-world wafer map dataset (WM-811K).

关键词： Deep learning Manifolds Noise reduction Feature extraction Manufacturing processes Learning systems Wafer map defect recognition deep learning stacked denoising autoencoder manifold regularization

来源：评论

学校读者我要写书评

暂无评论

Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification

引用

SIGNAL PROCESSING 2017年第0期130卷 377-388页

作者： Lu, Chen Wang, Zhen-Ya Qin, Wei -Li Ma, Jian Beihang Univ Sch Reliabil & Syst Engn Xueyuan Rd Beijing Peoples R China Beihang Univ Sci & Technol Reliabil & Environm Engn Lab Xueyuan Rd Beijing Peoples R China

Effective fault diagnosis has long been a research topic in the prognosis and health management of rotary machinery engineered systems due to the benefits such as safety guarantees, reliability improvements, and economical efficiency. This paper investigates an effective and reliable deep learning method known as stacked denoising autoencoder (SDA), which is shown to be suitable for certain health state identifications for signals containing ambient noise and working condition fluctuations. SDA has become a popular approach to achieve the promised advantages of deep architecture-based robust feature representations. In this paper, the SDA-based fault diagnosis method contains three successive steps: health states are first divided into training and testing groups for the SDA model, a deep hierarchical structure is then established with a transmitting rule of greedy training, layer by layer, where sparsity representation and data destruction are applied to obtain high-order characteristics with better robustness in the iteration learning. Validation data are finally employed to confirm the fault diagnosis results of the SDA, where existing health state identification methods are used for comparison. Rotating machinery datasets are employed to demonstrate the effectiveness of the proposed method. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Fault diagnosis stacked denoising autoencoder Health state identification Deep learning

来源：评论

学校读者我要写书评

暂无评论

Roller bearing fault diagnosis using stacked denoising autoencoder in deep learning and Gath-Geva clustering algorithm without principal component analysis and data label

引用

APPLIED SOFT COMPUTING 2018年 73卷 898-913页

作者： Xu, Fan Tse, Wai Tai Peter Tse, Yiu Lun City Univ Hong Kong Dept Syst Engn & Engn Management Tat Chee Ave Kowloon Hong Kong Peoples R China

Most deep learning models such as stacked autoencoder (SAE) and stacked denoising autoencoder (SDAE) are used for fault diagnosis with a data label. These models are applied to extract the useful features with several hidden layers, then a classifier is used to complete the fault diagnosis. However, these fault diagnosis classification methods are only suitable for tagged datasets. Actually, many datasets are untagged in practical engineering. The clustering method can classify data without a label. Therefore, a method based on the SDAE and Gath-Geva (GG) clustering algorithm for roller bearing fault diagnosis without a data label is proposed in this study. First, SDAE is selected to extract the useful feature and reduce the dimension of the vibration signal to two or three dimensions direct without principal component analysis (PCA) of the final hidden layer. Then GG is deployed to identify the different faults. To demonstrate that the feature extraction performance of the SDAE is better than that of the SAE and EEMD with the FE model, the PCA is selected to reduce the dimension of eigenvectors obtained from several previously hidden layers, except for the final hidden layer. Compared with SAE and ensemble empirical mode decomposition (EEMD)-fuzzy entropy (FE) models, the results show that as the number of the hidden layers increases, all the fault samples under different conditions are separated better by using SDAE rather than those feature extraction models mentioned. In addition, three evaluation indicators such as PC, CE, and classification accuracy are used to assess the performance of the method presented. Finally, the results show that the clustering effect of the method presented, and its classification accuracy are superior to those of the other combination models, including the SAE-fuzzy C-means (FCM)/Gustafson-Kessel (GK)/GG and EEMD-fuzzy entropy FE-PCA-FCM/GK/GG. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Deep learning stacked denoising autoencoder Gath-Geva clustering algorithm Roller bearing fault diagnosis

来源：评论

学校读者我要写书评

暂无评论

Automated feature learning for nonlinear process monitoring - An approach using stacked denoising autoencoder and k-nearest neighbor rule

引用

JOURNAL OF PROCESS CONTROL 2018年 64卷 49-61页

作者： Zhang, Zehan Jiang, Teng Li, Shuanghong Yang, Yupu Shanghai Jiao Tong Univ Dept Automat Minist Educ Syst Control & Informat Proc Key Lab Shanghai 200240 Peoples R China

Modern industrial processes have become increasingly complicated, consequently, the nonlinearity of data collected from these systems continues to increase. However, the feature extraction methods of existing process monitoring are not capable of extracting crucial features from these highly nonlinear data, which affects the performance of monitoring. In this paper, a novel nonlinear process monitoring method based on stacked denoising autoencoder (SDAE) and k-nearest neighbor (kNN) rule is proposed. Specifically, stacked denoising autoencoder is utilized to model the nonlinear process data and automatically extract crucial features. The original nonlinear space is then mapped to the feature space and the residual space via SDAE. Two new statistics in the above spaces are constructed by introducing the kNN rule with their corresponding control limits determined by kernel density estimation. Case studies on a nonlinear numerical system and the Tennessee Eastman benchmark process verify the effectiveness of the proposed method. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Deep learning Automated feature learning stacked denoising autoencoder k-Nearest neighbor rule Nonlinear process monitoring

来源：评论

学校读者我要写书评

暂无评论

Visual tracking based on stacked denoising autoencoder network with genetic algorithm optimization

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2018年第4期77卷 4253-4269页

作者： Hua, Weixin Mu, Dejun Guo, Dawei Liu, Hang Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China China Mobile Ltd Co Shaanxi Xian 710074 Shaanxi Peoples R China

Visual object tracking in dynamic environments with severe appearance variations is a significant problem in the computer vision field. This paper proposes a novel visual tracking algorithm that exploits the multiple level features learning ability of SDAE. There are two training stages for the SDAE network: Layer-wise pre-training and fine-tuning. In the pre-training stage, a two-layer sparse-coded method is used to represent the input image, then a multi-level image feature descriptor is obtained. In the fine-tuning stage, the connection weights and bias terms for back propagation are gathered via genetic algorithm. A logistic classification layer is added at the top of the encoder network to enable tracking within the well-established particle filter network. Experimental results confirm, both qualitatively and quantitatively, that the proposed method performs well in comparison against eight other state-of-the-art methods.

关键词： Object tracking stacked denoising autoencoder Sparsity dictionary encode Genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Multiscale intelligent fault detection system based on agglomerative hierarchical clustering using stacked denoising autoencoder with temporal information

引用

APPLIED SOFT COMPUTING 2020年 95卷 106525-106525页

作者： Yu, Jianbo Yan, Xuefeng East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

Deep learning-based process monitoring has achieved remarkable progress. Generally, a deep model is empirically selected before the data features are learned. In this study, the interpretability and suitability of stacked denoising autoencoder (SDAE) in process monitoring territory are theoretically analyzed and validated. Considering that the data will show different feature representations at different scales, such as overall outline, local information, and microscopic details, this study utilizes the concept of multiscale analysis to mine the feature information of raw data deeply in different scales. The multiscale analysis is performed on the basis of agglomerative hierarchical clustering and silhouette coefficient, which makes the analysis data characteristics-based and intelligently abandons the intervention of manual prior knowledge. Then, the SDAE models are established under each scale to learn the high-order and robust features from the data with noise and fluctuation, and all monitoring results of the different scales are integrated using the Bayesian inference. Finally, given the temporal information in sequence data, the state representation of previous events is embedded into the current decision through a sliding window. The numerical process, benchmark Tennessee Eastman and real steel plate process are used to analyze the superiority of the proposed method (MSDAE-TP) over other deep learning-based monitoring methods. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Process monitoring stacked denoising autoencoder Multiscale analysis Robust features Temporal information

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：