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2020 Chinese Intelligent Systems Conference

作者： Yin, Hongpeng Guo, Jiaxin Liao, Guobo Chai, Yi Chongqing Univ Chongqing 400044 Peoples R China

ISBN: (纸本)9789811584527;9789811584503;9789811584497

Both linear and nonlinear relationships are two typical characteristics among industrial process variables, and diagnosing a process with such complicated correlations among variables is indispensable. However, individual dictionary learning or autoencoder based method is hard to extract these complicated correlations well. The parallel label consistent KSVD-stacked autoencoder (P-LCKSVD-SAE) model is proposed to integrate the linear and nonlinear feature extraction for effective industrial process fault diagnosis. First, LCKSVD and SAE methods are applied to extract the linear and nonlinear features from industrial process. Second, a matrix fusion algorithm is proposed for combining these intrinsic fault features. Then, the SVM classifier is utilized to verify the effectiveness of the proposed model for fault classification. Finally, several case studies on Tennessee Eastman process demonstrate that the proposed P-LCKSVD-SAE fault diagnosis scheme is better than the conventional LC-KSVD as well as the SAE methods at performing industrial process fault diagnosis.

关键词： Nonlinear industrial process Fault diagnosis Label consistent KSVD stacked autoencoder

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Classification of Human Activity by Using a stacked autoencoder

Classification of Human Activity by Using a Stacked Autoenco...

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Medical Technologies National Conference (TIPTEKNO)

作者： Badem, Hasan Caliskan, Abdullah Basturk, Alper Yuksel, Mehmet Emin Erciyes Univ Bilgisayar Muhendisligi Bolumu Kayseri Turkey Erciyes Univ Biyomed Muhendisligi Bolumu Kayseri Turkey

ISBN: (纸本)9781509023868

This paper investigates the application of a deep neural network architecture that consists of stackted autoencoder with two autoencoders and a softmax layer for the purpose of human activity classification. Th performance of the proposed architecture is tested on a commonly used data set known as Human Activity Recognition Using Smartphones. It is observed that the proposed method yields better classification results than the representative state-of-the-art methods provided that the parameters of the deep network are suitably optimized.

关键词： Deep Neural Network stacked autoencoder Softmax Human Activity Recognition

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Batch Process Monitoring Based on Multi-stage Fourth Order Moment stacked autoencoder 32

Batch Process Monitoring Based on Multi-stage Fourth Order M...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Chen, Jin Pu, Wang Kai, Wang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China

ISBN: (纸本)9781728158556

Fault monitoring can find out-of-control conditions of equipment operation in a timely manner, which is essential for eliminating faults and for stable operation of industrial systems in batch processes. Many conventional data-driven fault detection methods focus less on the non-Gaussian and Multi-stage characteristics of batch process data, which may result in degradation of monitoring performance. In this paper, a Multi-stage Fourth Order Moment Staked autoencoder (M-FOM-SAE) is designed to solve the above problems. The proposed method firstly automatically determines the number of clusters and divides the batch process into multiple stages. After that, the FOM-SAE model is established in each sub-stage, which can not only effectively learn the nonlinear features of process data, but also extract the non-Gaussian information. The proposed strategy is applied to real-world industrial processes. Experimental results indicate that it can better capture the non-Gaussian and Multi-stage characteristics of process data, and improve the ability to monitor abnormalities.

关键词： Batch process Fault monitoring non-Gaussian Multi-stage stacked autoencoder

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Enhanced Fault Detection Using Deviation Degree Penalty with stacked autoencoder in Industry Process 9

Enhanced Fault Detection Using Deviation Degree Penalty with...

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9th IEEE Data Driven Control and Learning Systems Conference (DDCLS)

作者： Wang, Kai Zhou, Le Wang, Yalin Cent South Univ Sch Automat Changsha 410083 Peoples R China Zhejiang Univ Sci & Technol Sch Automat & Elect Engn Hangzhou 310023 Peoples R China

ISBN: (纸本)9781728159225

In a data-driven fault detection strategy, not only the data model, but the detection index also influences the detection performance a lot. Deep learning has a flexible and powerful ability for modeling strongly nonlinear processes with good generalization performance. However, lack of statistical interpretations, the detection indices have to resort to nonparametric density estimation methods in most of deep models. Thus, the control limits are severely influenced by a specific set of training data, causing a poor robustness. Simultaneously, the squashed activations in neural networks make the detection margin limited. To solve these problems, a novel sample deviation degree penalty strategy with stacked autoencoder is proposed to regularize the neurons and the reconstruction errors, which makes the neurons in each layer and the reconstruction error of different samples tend to be converged. Thus, the robustness and the detection margin are improved. Experiment data in a multiphase flow facility are used to verify the efficacy of the proposed algorithm.

关键词： Fault detection stacked autoencoder degrees of outliers regularization

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Research on Intelligent Diagnosis of Wear Faults of Centrifugal Pumps Based on stacked autoencoder 3

Research on Intelligent Diagnosis of Wear Faults of Centrifu...

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3rd International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Xiang, Mingsheng Li, Yingli Feng, Kun Beijing University of Chemical Technology Key Laboratory of Engine Health Monitoring-Control and Networking Ministry of Education Beijing China China Petroleum Safety and Environmental Protection Technology Research Institute Beijing China Beijing University of Chemical Technology Beijing Key Laboratory of High-End Mechanical Equipment Health Monitoring and Self-Recovery Beijing China

ISBN: (纸本)9781665492812

Mechanical fault diagnosis is very important in industry because early detection can avoid some dangerous situations, and not much research has been done on the diagnosis of wear faults in centrifugal pumps. With the rapid development of data analysis techniques, data-driven diagnosis methods are becoming increasingly popular. In this study, stacked autoencoder based method is proposed to solve the centrifugal pump seal wear fault diagnosis problem. The method extracts power spectral density features directly from the vibration signal and chunks the features, greatly reducing the training difficulty and improving the accuracy of the model. The effectiveness of the method is verified using a centrifugal pump dataset, and the results show that the method can diagnose not only the fault site but also the degree of wear. © 2022 IEEE.

关键词： power spectral density stacked autoencoder wear failure

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stacked autoencoder based Feature Compression for Optimal Classification of Parkinson Disease from Vocal Feature Vectors using Immune Algorithms

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第5期12卷 470-476页

作者： Kamalakannan, K. Anandharaj, G. Adhiparasakthi Coll Arts & Sci PG & Res Dept Comp Sci Kalavai TN India

Parkinson's disease (PD) is a neurological progressive disorder and is most common among people who are above 60 years old. It affects the brain nerve cells due to the deficiency of dopamine secretion. Dopamine acts as a neurotransmitter and helps in the movement of the body parts. Once brain cells/neurons start dying due to aging, then it will lead to a decrease in dopamine levels. The symptoms of Parkinson's are difficultly in doing regular/habitual movements, uncontrollable shaking of hands and limbs may encounter memory loss, stiff muscles, sudden temporary loss of control, etc. The severity of the disease will be worse if not diagnosed and treated at the early stages. This paper concentrates on developing Parkinson's disease diagnosing system using machine learning techniques and algorithms. Machine Learning is an integral part of artificial intelligence it takes huge data as input and train by making use of existing algorithms to understand the pattern of the data. Based on the recognized pattern, the machine will act accordingly without any human intervention. In this work, two major approaches have been employed to diagnose PD. Initially, 26 vocal data of PD affected and healthy individual datasets are obtained from the UCI Machine Learning data repository, are taken as initial raw data/features. In pre-processing, the mRMR feature selection algorithm is employed to minimize the feature count and increase the accuracy rate. The selected features will further be extracted using the stacked autoencoder technique to improve and increase the accuracy rate and quality of classification with reduced run time. K-fold cross-validation is used to evaluate the predictive capability of the model and the effectiveness of the extracted features. Artificial Immune Recognition System - Parallel (AIRS-P), an immune inspired algorithm is employed to classify the data from the extracted features. The proposed system attained 97% accuracy, outperforms the benchmarked algorithm

关键词： Immune algorithms Parkinson's disease stacked autoencoder airs-parallel machine learning

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Spatiotemporal stacked autoencoder based Soft Sensor Modeling for the Dow Data Challenge Problem 9

Spatiotemporal Stacked Autoencoder based Soft Sensor Modelin...

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9th IEEE Smart World Congress, SWC 2023

作者： Zhu, Xiuli Damarla, Seshu Kumar Huang, Biao University of Shanghai for Science and Technology Department of Optical-Electrical and Computer Engineering Shanghai China University of Alberta Department of Chemical and Materials Engineering AB Canada

ISBN: (纸本)9798350319804

Process data with characteristics such as strong nonlinearity, high dimensionality, cross-correlations and auto correlations pose a great challenge for data-driven soft sensor modeling. Albeit the conventional stacked autoencoder (SAE) is able to learn low dimensional nonlinear features from the process data, it fails to extract spatial and temporal features. To overcome this shortcoming, in the present work, spatiotemporal SAE (STSAE) is proposed based on convolutional and LSTM layers. Lower dimensional nonlinear features (spatial and temporal) extracted from STSAE are used to build the soft sensor. The effectiveness of the STSAE-based soft sensor is demonstrated in an industrial case study, the Dow data challenge problem. For the purpose of comparison, soft sensor models based on popular machine learning algorithms are developed and applied to the industrial case study. The obtained results demonstrate that the proposed STSAE based soft sensor yields more accurate estimations for impurity level at the primary column outlet of Dow process refining system. © 2023 IEEE.

关键词： autoencoder CNN Dow data challenge problem LSTM stacked autoencoder

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Highway tollgates traffic prediction using a stacked autoencoder neural network

Highway tollgates traffic prediction using a stacked autoenc...

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作者： OSKAR KARRMAN LINNEA OTTERLIND Chalmers University of Technology

学位级别：硕士

Traffic flow prediction is an important area of research with a great number of applications such as route planning and congestion avoidance. This thesis explored artificial neural network performance as travel time and traffic volume predictors. stacked autoencoder artificial neural networks were studied in particular due to recent promising performance in traffic flow prediction, and the result was compared to multilayer perceptron networks, a type of shallow artificial neural networks. The Taguchi design of experiments method was used to decide network parameters. stacked autoencoder networks generally did not perform better than shallow networks, but the results indicated that a bigger dataset could favor stacked autoencoder networks. Using the Taguchi method did help cut down on number of experiments to test, but choosing network settings based on the Taguchi test results did not yield lower error than what was found during the Taguchi tests.

关键词： stacked autoencoder multilayer perceptron neural network traffic prediction traffic flow taguchi

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Remaining useful life prediction method based on stacked autoencoder and generalized wiener process for degrading bearing

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第10期35卷 106132-106132页

作者： Chen, Zhe Li, Yonghua Gong, Qi Wang, Denglong Yin, Xuejiao Dalian Jiaotong Univ Coll Locomot & Rolling Stock Engn Dalian 116028 Peoples R China Dalian Jiaotong Univ Key Lab Railway Ind Safety Serv Key Technol Highsp Dalian 116028 Peoples R China

Remaining useful life (RUL) prediction using deep learning networks primarily produces point estimates of RUL, but capturing the inherent uncertainty in RUL prediction is difficult. The use of the stochastic process approach can reflect the uncertainty in RUL predictions. However, the amount of data generated during equipment operation cannot be effectively utilized. This paper aims to propose an adaptive RUL prediction method tailored for extensive datasets and prediction uncertainty, effectively harnessing the strengths of deep learning methods in managing massive data and stochastic process techniques in quantifying uncertainties. RUL prediction method, based on stacked autoencoder (SAE) combined with Generalized Wiener Process, employs SAE to extract profound underlying features from the monitoring signals. Principal component analysis (PCA) is then used to select highly trending features as inputs. The output of PCA accurately reflects health status. A Generalized Wiener Process is used to construct a model for the evolution of the health indicators. The estimation values for the model parameters are determined using the Maximum Likelihood Estimation method. Furthermore, an adaptive update is performed based on Bayesian theory. Utilizing the sense of the first hitting time concept, the Probability Density Function for RUL prediction is derived accurately. Finally, the effectiveness and superiority of the proposed method is verified using numerical simulations and experimental studies of bearing degradation data. The method improves the life prediction accuracy while reducing the prediction uncertainty.

关键词： remaining useful life prediction stacked autoencoder generalized wiener process degradation modeling feature extraction

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An Integrative Remote Sensing Application of stacked autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

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REMOTE SENSING 2020年第7期12卷 1073-1073页

作者： Pyo, JongCheol Duan, Hongtao Ligaray, Mayzonee Kim, Minjeong Baek, Sangsoo Kwon, Yong Sung Lee, Hyuk Kang, Taegu Kim, Kyunghyun Cha, YoonKyung Cho, Kyung Hwa Ulsan Natl Inst Sci & Technol Sch Urban & Environm Engn Ulsan 689798 South Korea Chinese Acad Sci Nanjing Inst Geog & Limnol Key Lab Watershed Geog Sci Nanjing 210008 Peoples R China Natl Inst Environm Res Water Qual Assessment Res Div Environm Res Complex Incheon 22689 South Korea Natl Inst Environm Res Watershed & Total Load Management Res Div Incheon 22689 South Korea Univ Seoul Sch Environm Engn Seoul 130743 South Korea

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash-Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application.

关键词： deep learning stacked autoencoder cyanobacteria hyperspectral image feature extraction dimensionality reduction

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