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A mixture of probabilistic predictable feature analysis for multi-mode dynamic process monitoring

JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2023年第1期143卷

作者： Fan, Wei Ren, Shaojun Yu, Cong Yu, Haiquan Wang, Peng Si, Fengqi Southeast Univ Key Lab Energy Thermal Convers & Control Minist Educ Nanjing 210096 Jiangsu Peoples R China Jianghan Univ Sch Intelligent Mfg Wuhan 430056 Hubei Peoples R China

In modern industrial processes, the demand for safety and reliability is increasingly prioritized, spawning quantities of related research on process monitoring models. It is significant for process monitoring to consider multi-mode operating conditions simultaneously. This work proposes an efficient method based on multi-mode probabilistic predictable feature analysis (MPPFA) for complex industrial process monitoring. A strategy combining deep auto-encoder (DAE) and Gaussian mixture model (GMM) is first designed to extract low dimensional features and identify possible running modes. In line with the obtained features, original training samples are classified into corresponding Gaussian distributions with reasonable probabilities. Further, local PPFA models are established based on the separated training batches. Five monitoring statistics, including T2P, SPEP, and DIP for PPFAs, T2D and SPED for DAE, are defined to detect abnormalities. The proposed method is first applied to a three-phase flow facility, and its superiority over comparison methods has been verified. The effectiveness of the proposed method is further demonstrated through an experiment on a practical coal pulverising system.

关键词： Probabilistic predictable feature analysis Dynamic process monitoring Multi-mode industrial process Gaussian mixture model deep auto-encoder

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Cardiovascular risk detection using Harris Hawks optimization with ensemble learning model on PPG signals

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第8期17卷 4503-4512页

作者： Divya, R. Shadrach, Finney Daniel Padmaja, S. PSG Coll Technol Dept Elect & Elect Engn Coimbatore India KPR Inst Engn & Technol Dept Elect & Commun Engn Coimbatore Tamil Nadu India Keshav Mem Inst Technol Hyderabad India

Cardiovascular (CVD) risk detection using Electrocardiography (ECG) and photoplethysmography (PPG) signals is an emerging field of research in the area of machine learning and biomedical engineering. ECG is an electrical measurement that captures cardiac actions and is the gold standard for identifying CVD. But, ECG cannot be used for continuous cardiac monitoring because of its necessity for user participation. PPG is an optically attained signal for detecting blood volume changes in the microvascular bed of tissues. deep learning (DL) methods have shown remarkable performance in predicting and detecting CVD disease using PPG signals. Therefore, this study designs a new Cardiovascular Risk Detection using Harris Hawks Optimization with Ensemble Learning (CRDHHO-EL) model on PPG Signals. The presented CRDHHO-EL technique examines the PPG signals to identify the risks of cardiovascular diseases. To accomplish this, the CRDHHO-EL technique uses an ensemble of three classifiers, namely deep belief network (DBN), deep auto-encoder (DAE), and extreme learning machine (ELM) models. Moreover, the HHO algorithm is used for adjusting the hyperparameter values of the classifier models, boosting overall performance. The experimental result analysis of the CRDHHO-EL on the open-access dataset showcases the significant performance of the CRDHHO-EL technique over other existing approaches.

关键词： Cardiovascular disease deep auto-encoder PPG signals Computer aided detection deep learning

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Development of tool wear condition on-line monitoring method for impeller milling based on new data processing approach and DAE-BP-ANN-integrated modeling

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2024年第1-2期238卷 124-136页

作者： Zou, Zheng Gao, Xu Lei, Sicong Zhang, Hao Min, Rongcheng Yang, Yong Chongqing Univ Technol Sch Mech Engn Chongqing Peoples R China Chongqing Machine Tool Grp Co Ltd Chongqing Peoples R China Chongqing Univ Technol Sch Mech Engn 69 Honghuang Ave Chongqing 400054 Peoples R China

Tool wear condition monitoring plays an important role in the maintaining machining accuracy and machining efficiency of complex surface parts. In this study, a new on-line tool wear monitoring method based on a self-developed data processing approach for the impeller milling was proposed. To achieve that, a new tool wear experimental platform was first built to collect both the spindle current signal and thermal deformation data in entire life cycle of cutter. Based on collected data, features of the time domain, frequency domain and time-frequency domain were extracted indiscriminately, and a 38 x 156 feature-sample set was subsequently established. To further reduce the dimensions of this feature-sample set and rise its characterization capability, the feature set was further processed using the sensitivity analysis and deep auto-encoder algorithm. Finally, 12 synthesized features were filtered out and then used to build the mapping model of signal synthesized features to different tool wear conditions by adopting the structural artificial neural network (ANN) integrated with back propagation (BP) algorithm. To verify the reliability of the proposed BP-ANN-integrated tool wear condition monitoring model, another comparison analysis of different data processing approaches was conducted. The comparison results showed that the proposed method for tool wear condition online monitoring had reliable performance and the recognition accuracy was 88.9%.

关键词： Tool wear condition monitoring thermal deformation deep auto-encoder artificial neural network back propagation

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Unsupervised fault detection approach based on depth auto-encoder for photovoltaic power generation system

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第2期24卷 849-861页

作者： Zhang, Jun Chen, Zongren Wu, Weimei Shao, Liuyang Deng, Kaihuan Gao, Shixiong Guangdong Polytechn Sci & Technol Comp Engn Tech Coll Artificial Intelligence Coll Zhuhai 519090 Guangdong Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China

Fault detection for photovoltaic power generation system is a challenging problem in condition monitoring and troubleshooting, which aims to maintain the safe operation of equipment and improve the benefit of photovoltaic industry. Aiming at the problems of frequent failures of photovoltaic power generation system, large amount of operating data and difficult to obtain fault samples, we propose an unsupervised fault detection approach for photovoltaic power generation system via bidirectional long/short memory deep auto-encoder which combines the auto-encoder in deep learning with the Bi-directional Long Short-Term Memory (BiLSTM). Specifically, We first take the statistical feature enhanced as the input of an auto-encoder based on BiLSTM. Then, we build a simulation model of Grid-connected PV system. Finally, we use the operation results under normal conditions to train the fault detection model to obtain the reconstruction error and determine the fault detection threshold, so as to judge the anomalies of the photovoltaic power generation system. We simulate the shadow occlusion fault and verify the effectiveness of the proposed method, and the fault detection accuracy of 0.95 is achieved. Compare with other models, the results show that it could set up better dependence on multi-dimensional data in time sequences, effectively testing solar panel failures and solving insufficient data labels problems.

关键词： Fault detection deep learning photovoltaic power generation system deep auto-encoder bidirectional long/short memory

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A physics-constrained deep learning based approach for acoustic inverse scattering problems

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2022年 164卷 108190-108190页

作者： Wu, Rih-Teng Jokar, Mehdi Jahanshahi, Mohammad R. Semperlotti, Fabio Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA Purdue Univ Sch Mech Engn W Lafayette IN 47907 USA Purdue Univ Sch Elect & Comp Engn Courtesy W Lafayette IN 47907 USA

The control of acoustic and elastic waves via engineered materials has several important real world applications such as non-destructive evaluation of structural components, synthesis of biomedical devices, high-resolution imaging, and remote sensing. Being formulated as inverse problems, all these applications share as a common denominator the need for efficient solution methodologies. Available techniques, mostly based on conventional optimization approaches, have shown some significant limitations in terms of the ability to explore a vast design space and to limit the computation burden. In this study, a novel deep auto-encoder (DAE) based approach is proposed in order to solve a benchmark inverse problem consisting in designing assemblies of acoustic scattering elements capable of molding an incoming plane wave into a target (user-defined) downstream pressure distribution. The proposed approach is validated numerically through three design scenarios, involving either a single or multiple scatterer configuration, and target pressure fields defined at different frequencies. The proposed network consists of a geometry estimator and a DAE that imposes constraints due to the physics of the problem on the geometry estimator during the learning process which leads to more robust design. By joint optimization, the estimation of scatterer geometry is strengthened with the latent representations of the target pressure field learned by the DAE. For a trained network, the design inference is quasi-instantaneous given a target 2D pressure field. The generalization capability of the proposed network is further explored by using a dataset generated based on scatterers having new shapes.

关键词： Material design Inverse problems Acoustic wave scattering Machine learning deep auto-encoder

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A novel deep learning-based fault diagnosis algorithm for preventing protection malfunction

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年 144卷

作者： Hu, Jiaxiang Liu, Zhou Chen, Jianjun Hu, Weihao Zhang, Zhenyuan Chen, Zhe Univ Elect Sci & Technol China Xiyuan Ave 2006 Chengdu 611731 Peoples R China Siemens Gamesa Renewable Energy A S Elektrovej 325 DK-2800 Lyngby Denmark Aalborg Univ Fredrik Bajers Vej 7K DK-9220 Aalborg Denmark

To prevent serious malfunctions and reduce the impact of faults during an emergency state of a power system, protection systems are required to have disturbance and fault state identification abilities. In this study, a novel fault diagnosis framework based on deep learning with anti-disturbance ability is proposed to identify the fault state and fault type information, even under the influence of system disturbance. The framework consists of two parts: unsupervised and supervised learning. Specifically, an unsupervised deep auto-encoder (DAE) is applied for offline feature selection and data cleaning. The DAE can extract key fault features and significantly improve the fault detection accuracy. Furthermore, two supervised convolutional neural networks are used to learn key fault feature extraction online from complex operation information in power systems and assess the fault situ-ation and type. Using case studies, the proposed method was implemented and compared with existing intelli-gent methods. The results indicate that the proposed framework has a better performance in terms of fault state identification and protection malfunction prevention.

关键词： DL deep auto-encoder Fault diagnosis Unsupervised and supervised learning Disturbance Malfunction prevention

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Elastic deep Sparse Self-Representation Subspace Clustering Network

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NEURAL PROCESSING LETTERS 2024年第2期56卷 58-58页

作者： Wang, Qiaoping Chen, Xiaoyun Li, Yan Lin, Yanming Fuzhou Univ Sch Math & Stat Fuzhou 350116 Fujian Peoples R China

Subspace clustering model based on self-representation learning often use l1,l2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _1, \ell _2$$\end{document} or kernel norm to constrain self-representation matrix of the dataset. In theory, l1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _1$$\end{document} norm can constrain the independence of subspaces, but which may lead to under-connection because the sparsity of the self-representation matrix. l2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _2$$\end{document} and nuclear norm regularization can improve the connectivity between clusters, but which may lead to over-connection of the self-representation matrix. Because a single regularization term may cause subspaces to be over or insufficiently divided, this paper proposes an elastic deep sparse self-representation subspace clustering network (EDS-SC), which imposes sparse constraints on deep features, and introduces the elastic network regularization mixed l1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _1$$\end{document} and l2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargi

关键词： Subspace clustering Representation learning deep auto-encoder Elastic net regularization deep sparse feature

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Granular space, knowledge-encoded deep learning architecture and remote sensing image classification

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 92卷 103647-103647页

作者： Meher, Saroj K. Indian Stat Inst Bangalore Ctr Syst Sci & Informat Unit Bangalore Karnataka India

Hand-crafted features of remotely sensed (RS) image require the involvement of expensive human experts for classification. This factor motivates for designing the classification model with representative feature learning-based deep architecture to automate the feature extraction process and improve the generalization capability of the model. With this reasoning, we propose a deep auto-encoder neural network (NN) architecture with knowledge-encoded granular space for the classification of RS images. The network works with wavelet-rough granulated spaces and its architecture is designed with the encoded domain knowledge that strategically initializes the network parameters. Mostly, the learning time and performance of deep auto-encoders are persuaded by randomly selected weights and thus, we aim here to minimize these efforts with the domain knowledge. Neighborhood rough sets (NRS) are used to encode the domain knowledge and explore the contextual information for improved decision. We perform the knowledge-encoding operation for all stages of the auto-encoder. The proposed model thus exploits the mutual merits of deep network, wavelet-rough granular space and knowledge-encoding method. Comparative experimental results with multispectral and hyperspectral RS images demonstrate the superiority of our model to the related advanced methods.

关键词： deep auto-encoder Remote sensing image classification Information granulation Wavelet transform Rough sets Knowledge-encoding

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deep Learning-Based Dependability Assessment Method for Industrial Wireless Network ⁎

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IFAC-PapersOnLine 2019年第24期52卷 219-224页

作者： Danfeng Sun Sarah Willmann Institut f. Automation und Kommunikation e.V. 39106 Magdeburg Germany Institut f. Automation und Kommunikation e.V. 39106 Germany

Techniques on 5G and Internet of things bring a strong potential paradigm shift to wireless communication applications in industrial domain. Hence, there is a strong need for quantitative dependability assessment in these applications. However, with the evergrowing complexity and amount of wireless communication systems, their dependability relevant parameters also increase rapidly. In addition, the deep neural network has advantages on high dimensional data process. Hence, a deep learning-based dependability assessment method is proposed to address the issue, wherein a deep auto-encoder based approach is proposed to reduce data dimension and to obtain the data codes, and DBSCAN is used to cluster these codes. An experimental environment is built for collecting data set on the Multifaces, and a rough classification method is proposed to obtain a superior deep encoder model. Based on the superior model and DBSCAN, the data set are mainly divided into four dependability clusters.

关键词： DBSCAN deep auto-encoder dependability assessment machine learning industrial wireless network

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deep Learning-Based Two-Step Approach for Intrusion Detection in Networks

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International Journal of Internet and Distributed Systems 2024年第2期6卷 25-39页

作者： Kamagaté Beman Hamidja Kanga Koffi Kouassi Adless Olivier Asseu Souleymane Oumtanaga Ecole Suprieure Africaine des Technologies de lInformation et de la Communication (ESATIC) LASTIC Laboratory of ESATIC Abidjan Cte dIvoire UMRI STI INPHB INPUMRI STI INPHB INP-Houphout Boigny Yamoussoukro Cte dIvoire UMRI MSN INPHB INP-Houphout Boigny Yamoussoukro Cte dIvoire

Intrusion Detection Systems (IDS) are essential for computer security, with various techniques developed over time. However, many of these methods suffer from high false positive rates. To address this, we propose an approach utilizing Recurrent Neural Networks (RNN). Our method starts by reducing the dataset’s dimensionality using a deep auto-encoder (DAE), followed by intrusion detection through a Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed DAE-BiLSTM model outperforms Random Forest, AdaBoost, and standard BiLSTM models, achieving an accuracy of 0.97, a recall of 0.95, and an AUC of 0.93. Although BiLSTM is slightly less effective than DAE-BiLSTM, both RNN-based models outperform AdaBoost and Random Forest. ROC curves show that DAE-BiLSTM is the most effective, demonstrating strong detection capabilities with a low false positive rate. While AdaBoost performs well, it is less effective than RNN models but still surpasses Random Forest.

关键词： Cybersecurity CICIDDS2017 Intrusion Detection BiLSTM deep auto-encoder

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