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stacked denoising autoencoder-based feature learning for out-of-control source recognition in multivariate manufacturing process

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2019年第1期35卷 204-223页

作者： Yu, Jianbo Zheng, Xiaoyun Wang, Shijin Tongji Univ Sch Mech Engn Shanghai 200082 Peoples R China Tongji Univ Sch Econ & Management Shanghai Peoples R China

In multivariate statistical process control (MSPC), regular multivariate control charts (eg, T-2) are shown to be effective in detecting out-of-control signals based upon an overall statistic. But these charts do not relieve the need for multivariate process pattern recognition (MPPR). MPPR would be very useful for quality operators to locate the assignable causes that give rise to out-of-control situation in multivariate manufacturing process. Deep learning has been widely applied and obtained many successes in image and visual analysis. This paper presents an effective and reliable deep learning method known as stacked denoising autoencoder (SDAE) for MPPR in manufacturing processes. This study will concentrate on developing a SDAE model to learn effective discriminative features from the process signals through deep network architectures. Feature visualization is performed to explicitly present feature representations of the proposed SDAE model. The experimental results illustrate that the proposed SDAE model is capable of implementing detection and recognition of various process patterns in complicated multivariate processes. Analysis from this study provides the guideline in developing deep learning-based MSPC systems.

关键词： deep learning feature learning multivariate manufacturing process process pattern recognition stacked denoising autoencoder

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stacked denoising autoencoder for missing traffic data reconstruction via mobile edge computing

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NEURAL COMPUTING & APPLICATIONS 2023年第19期35卷 14259-14274页

作者： Dai, Penglin Luo, Jingtao Zhao, Kangli Xing, Huanlai Wu, Xiao Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Engn Res Ctr Sustainable Urban Intelligent Transpo Minist Educ Chengdu Peoples R China Southwest Jiaotong Univ Tangshan Inst Tangshan 063000 Peoples R China

Traffic sensing system requires to periodically collect spatial-temporal traffic data distributed among road networks, which results in overhigh bandwidth consumption and storage cost in a large-scale road network. Several compressive sensing-based algorithms are proposed to reconstruct missing traffic data with limited traffic observation. However, there still exist great challenges to be addressed. First, these existing algorithms are always iteration-based, whose time complexity will explosively increase with the growth of network scale. Furthermore, these algorithms have to be re-executed even if only a small fraction of data changes, which is not suitable for dynamic traffic environments. To overcome these issues, we investigate a novel service architecture of traffic sensing based on mobile edge computing where collected data is pre-processed at the edge node and reconstructed at cloud servers, respectively. On this basis, we formulate the problem of Missing Traffic Data reconstruction (MTDR), which aims at maximizing data reconstruction accuracy within limited observation data. Further, we develop a deep-learning-based algorithm called stacked denoising autoencoder for MTDR (SDAE-MTDR), where three denoising autoencoders are trained in order and then stacked together for parameter fine-tuning based on cross-entropy-based loss function. Finally, we conduct comprehensive performance evaluation based on realistic vehicular traces and the simulation results demonstrate the superiority of the proposed algorithm compared with competitive solutions.

关键词： Missing traffic data reconstruction stacked denoising autoencoder Mobile edge computing

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stacked denoising autoencoder Enhanced Polar Codes Over Rayleigh Fading Channels

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IEEE WIRELESS COMMUNICATIONS LETTERS 2020年第3期9卷 354-357页

作者： Li, Jianxiu Cheng, Wenchi Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

Polar codes, with low encoding/decoding complexity and capacity-achieving potential, have drawn much attention recently. It is very critical to study the impact of fading on polar codes implementation into wireless communications. Existing research works on polar codes over the fading channel use simplified fading channel models while further reducing the frame error rate is highly needed. In this letter, using the structure of polar codes we propose the deep learning based scheme for polar codes over Rayleigh fading channels. Simulation results verify that our proposed scheme can significantly decrease the frame error rate for polar codes over Rayleigh fading channels.

关键词： Polar codes deep learning stacked denoising autoencoder Rayleigh fading channel

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stacked denoising autoencoder With Density-Grid Based Clustering Method for Detecting Outlier of Wind Turbine Components

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IEEE ACCESS 2019年 7卷 13078-13091页

作者： Sun, Zexian Sun, Hexu Hebei Univ Technol Sch Articial Intelligence Tianjin 300401 Peoples R China

Different types of outliers have existed in the monitoring data of wind turbines, which are not conducive to the follow-up data mining. However, the complex inner characteristics of the monitoring data pose major challenges to detect the outliers. To address this problem, an unsupervised outlier detection approach combining stacked denoising autoencoder (SDAE) and density-grid-based clustering method is proposed. First, the characteristics of the outliers in supervisory control and data acquisition data caused by different reasons are analyzed. Then, the SDAE is utilized to extract features by training the original data. Furthermore, the density-grid-based clustering method is applied to achieve the clustering results. Window width is added to classify the outliers as isolated outliers, missing data, and fault data according to the duration of abnormal data. The monitoring data of four wind turbines are sampled as the training data to demonstrate the effectiveness of the proposed method. The results show that the proposed model can effectively identify the isolated outliers, missing data, and fault information in the high dimensional data set by unsupervised learning.

关键词： Density-grid based clustering outlier detection stacked denoising autoencoder unsupervised learning

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stacked denoising autoencoder for infrared thermography image enhancement 19

Stacked denoising autoencoder for infrared thermography imag...

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IEEE 19th International Conference on Industrial Informatics (INDIN)

作者： Wei, Ziang Fernandes, Henrique Tarpani, Jose Ricardo Osman, Ahmad Maldague, Xavier Univ Appl Sci Saarbrucken Sch Engn Saarbrucken Germany Univ Fed Uberlandia Fac Comp Uberlandia MG Brazil Univ Sao Paulo Sao Carlos Sch Engn Sao Carlos Brazil Univ Appl Sci Saarbrucken Algorithms Signal & Data Proc Fraunhofer IZFP Inst Nondestruct Testing Sch Engn Saarbrucken Germany Univ Laval Dept Elect Engn & Comp Engn Quebec City PQ Canada

ISBN: (纸本)9781728143958

Pulsed thermography is one of the most popular thermography inspection methods. During an experiment of pulsed thermography, a specimen is quickly heated, and infrared images are captured to provide information about the specimen's surface and subsurface conditions. Adequate transformations are usually performed to enhance the contrast of the thermal images and to highlight the abnormal regions before these thermal images are visually inspected. Given that deep neural networks have been a success in computer vision in the past few years, a data contrast enhancement approach with stacked denoising autoencoder (DAE) is proposed in this paper to enhance the abnormal regions in the thermal frames gathered by pulsed thermography. Compared to the direct principal component thermography, the proposed method can enhance the abnormalities evidently without weakening important details.

关键词： stacked denoising autoencoder pulsed thermography PCT deep learning non-destructive evaluation

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stacked denoising autoencoder for Feature Representation Learning in Pose-Based Action Recognition 3

Stacked Denoising Autoencoder for Feature Representation Lea...

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3rd IEEE Global Conference on Consumer Electronics (GCCE)

作者： Budiman, Arif Fanany, Mohamad Ivan Basaruddin, Chan Univ Indonesia Fac Comp Sci Depok West Java Indonesia

ISBN: (纸本)9781479951468

In this paper, we studied stacked denoising Auto-encoder(SDA) model for Human pose-based action recognition. We used public dataset Chalearn 2013 which contains Italian body language actions from 27 persons. We studied two model of SDA for pose clustering: 1) Traditional SDA with epoch and Neural Network supervised classifier and 2) Marginalized SDA which faster and ELM supervised classifier. We used supervised classifier by using initial cluster data from K-means. We deployed global tuning that updating the weight during iterative learning.

关键词： feature extraction image classification image denoising image motion analysis learning (artificial intelligence) multilayer perceptrons pattern clustering pose estimation ELM supervised classifier Italian body language actions SDA model epoch classifier feature representation learning human pose-based action recognition iterative learning k-means clustering marginalized SDA multilayer perceptron neural network supervised classifier pose clustering stacked denoising autoencoder Accuracy Joints Neural networks Noise reduction Training Tuning

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stacked denoising autoencoder and Dropout Together to Prevent Overfitting in Deep Neural Network 8

Stacked Denoising Autoencoder and Dropout Together to Preven...

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Proceedings 2015 8 International Congress on Image and Signal Processing (CISP)

作者： Liang, Jianglin Liu, Ruifang Beijing Univ Posts & Telecommun Sch Informat & Commun Sci Beijing 100876 Peoples R China

ISBN: (纸本)9781467390989

Deep neural network has very strong nonlinear mapping capability, and with the increasing of the numbers of its layers and units of a given layer, it would has more powerful representation ability. However, it may cause very serious overfitting problem and slow down the training and testing procedure. Dropout is a simple and efficient way to prevent overfitting. We combine stacked denoising autoencoder and dropout together, then it has achieved better performance than singular dropout method, and has reduced time complexity during fine-tune phase. We pre-train the data with stacked denoising autoencoder, and to prevent units from co-adapting too much dropout is applied in the period of training. At test time, it approximates the effect of averaging the predictions of many networks by using a network architecture that shares the weights. We show the performance of this method on a common benchmark dataset MNIST.

关键词： deep neural network stacked denoising autoencoder dropout regulation method combination

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Urban built-up areas extraction by the multiscale stacked denoising autoencoder technique

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JOURNAL OF APPLIED REMOTE SENSING 2020年第3期14卷

作者： Mi, Xiaofei Cao, Weijia Yang, Jian Li, Zhenghuan Zhang, Yazhou Li, Qianjing Sun, Zhensheng Zhan, Yulin Chinese Acad Sci Aerosp Informat Res Inst Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Tsinghua Univ Sch Econ & Management Beijing Peoples R China China Fortune Land Dev Ind Investment Co Ltd Beijing Peoples R China LuDong Univ Sch Resources & Environm Engn Yantai Shandong Peoples R China Taiyuan Univ Technol Coll Min Engn Taiyuan Shanxi Peoples R China

stacked denoising autoencoder (SDAE) model has a strong feature learning ability and has shown great success in the classification of remote sensing images. However, built-up area (BUA) information is easily interfered with by broken rocks, bare land, and other features with similar spectral features. SDAEs are vulnerable to broken and similar features in the image. We propose a multiscale SDAE model to overcome these problems, which can extract BUA features in different scales and recognize the type of land object from multiple scales. The model effectively improves the recognition rate of BUA. The experimental results show that our algorithm can resist the disturbance information, and the classification accuracies are better than support vector machine, backpropagation, random forests, and SDAE. Then we investigate an application in Wuhan (China) metropolitan area analysis with the classification results of our algorithm. The range of the metropolitan area is 1.5-h isochronous circle calculated by Tencent map big data and is divided into three layers: core metropolitan area, subcore metropolitan area, and daily metropolitan. Finally, from the comprehensive statistical data and traffic data, we know that the Wuhan metropolitan area has a "target-shaped" distribution structure radiating outward from the core metropolitan area. It includes five metropolitan development corridors: Wuhan-Huanggang, Wuhan-Xiaogan-Suizhou, Wuhan-Ezhou-Huangshi, Wuhan-Xiantao-Tianmen, and Wuhan- Xianan-Chibi. The corridor is of great significance to the development of metropolitan areas. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

关键词： multiscale stacked denoising autoencoder built-up areas isochronous circle metropolitan area

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A novel fault early warning method for centrifugal blowers based on stacked denoising autoencoder and transfer learning

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JOURNAL OF MANUFACTURING SYSTEMS 2024年 76卷 443-456页

作者： Zhang, You Li, Congbo Tang, Ying Zhang, Xu Zhou, Feng Chongqing Univ State Key Lab Mech Transmiss Adv Equipment Chongqing 400044 Peoples R China Rowan Univ Dept Elect & Comp Engn Glassboro NJ 08028 USA

Centrifugal blowers are easy to get faults due to the harsh working environment, and appropriate fault early warning is of great significance for predictive maintenance. Traditional fault early warning methods have poor resistance and feature learning ability in dealing with multivariate data with noise, and cannot achieve domain adaptation in different working environments. Aimed at solving these problems, this paper proposes a novel fault early warning method for centrifugal blowers based on stacked denoising autoencoder with sliding window (SWSDAE) and transfer learning. The developed SW-SDAE model can effectively learn representative degradation features and temporal dependence from multivariate time-series data with noise. The reconstruction errors of SW-SDAE are used to construct the health indicators, which accurately characterizes the health status of the centrifugal blower. Meanwhile, transfer learning is employed to solve the problem of domain adaptation for different working environments. The established source domain warning model is successfully transferred to the target domain by minimizing the maximum mean discrepancy. When the health indicator exceeds the warning threshold, a fault early warning is performed. Experimental results demonstrate that the developed SW-SDAE warning model integrating transfer learning significantly resists the interference of noise and improves the domain adaptability for different working conditions. The proposed method achieves fault early warning 5.67 h without false alarms before failure and shows superior warning performance compared with traditional warning methods.

关键词： Fault early warning stacked denoising autoencoder Transfer learning Centrifugal blowers

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Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2017年第6期11卷 687-695页

作者： Xia, Min Li, Teng Liu, Lizhi Xu, Lin de Silva, Clarence W. Univ British Columbia Dept Mech Engn Ind Automat Lab Vancouver BC Canada Istuary Innovat Grp Vancouver BC Canada

Condition monitoring and fault diagnosis are important for maintaining the system performance and guaranteeing the operational safety. The traditional data-driven approaches mostly incorporate well-defined features and methodologies such as supervised artificial intelligence algorithms. Prior knowledge of possible features and a large quantity of labelled condition data are needed. Besides, many traditional approaches require rebuilding or a retraining of the original model to diagnosis new conditions. The present study proposes an intelligent fault diagnosis approach that uses a deep neural network (DNN) based on stacked denoising autoencoder. Representative features are learned by applying the denoising autoencoder to the unlabelled data in an unsupervised manner. A DNN is then constructed and fine-tuned with just a few items of labelled data. The trained DNN achieves high performance in fault classification. Furthermore, new conditions can be correctly classified by simply fine-tuning the trained DNN model using a small amount of labelled data under the new conditions. The effectiveness of the proposed approach is evaluated using a case study of fault diagnosis of a bearing unit. The results indicate that the proposed method can extract representative features from massive unlabelled data on the system condition and achieve high performance in fault diagnosis.

关键词： fault diagnosis signal denoising encoding neural nets feature extraction unsupervised learning signal classification signal representation intelligent fault diagnosis approach unsupervised feature learning stacked denoising autoencoder condition monitoring operational safety data-driven approach supervised artificial intelligence algorithms deep neural network fault classification trained DNN model representative feature extraction

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