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GRU-based stacked sparse autoencoder with attention mechanism for process monitoring

CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2025年第0期

作者： Miao, Zengdi Wu, Ping Wu, Zhenquan Jia, Xiangjun Xu, Hui Jiang, Jian Zhejiang Sci Tech Univ Sch Informat Sci & Engn 5 Second Ave Hangzhou 310018 Peoples R China Zhejiang Sanhua Automot Components Co Ltd Hangzhou Peoples R China Hangzhou Dahe Thermo Magnet Co Ltd 777 Binkang Rd Hangzhou 310053 Peoples R China Bluetron Digital Technol Co LTD Hangzhou Peoples R China Zhejiang Deli Machine Manufacturer Co Ltd Lishui Peoples R China

To ensure the operation safety of industrial processes, process monitoring techniques have been receiving considerably increasing attention from both academia and industry. The advent of deep learning has revolutionized data-driven process monitoring by effectively dealing with the inherent nonlinearity of industrial processes. In this work, a gated recurrent unit-based stacked sparse autoencoder with attention mechanism (GRU-SSAE-AM) model is developed for process monitoring. This model leverages the power of recurrent neural networks in conjunction with the sparse stacked autoencoder to tackle the temporal and nonlinear features present in process data. To enhance the model's ability to distill critical information indicative of process status, an attention mechanism is naturally integrated within the encoder-decoder framework. Experiments on an industrial benchmark of Tennessee Eastman process (TEP) and a real blast furnace ironmaking process (BFIP) are carried out to verify the capability and effectiveness of the proposed GRU-SSAE-AM based process monitoring method by comparison with other related methods.

关键词： attention mechanism blast furnace ironmaking process GRU process monitoring stacked sparse autoencoder

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Early identification of out-of-roundness damage wheels in railway freight vehicles using a wayside system and a stacked sparse autoencoder

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VEHICLE SYSTEM DYNAMICS 2025年第2期63卷 232-257页

作者： Jorge, T. Magalhaes, J. Silva, R. Guedes, A. Ribeiro, D. Vale, C. Meixedo, A. Mosleh, A. Montenegro, P. Cury, A. Polytech Porto Sch Engn CONSTRUCT LESE Porto Portugal Univ Porto Fac Engn CONSTRUCT LESE Porto Portugal Univ Fed Juiz de Fora Grad Program Civil Engn Juiz De Fora Brazil

Early identification of wheel defects can prevent serious damage to railways, considerably lowering maintenance costs for both railway administrations and rolling stock operators. Within this context, an unsupervised methodology based on artificial intelligence techniques is presented, which allows the detection and classification of out-of-roundness damage wheels, such as wheel flats and polygonal wheels, based on dynamic responses induced on the track by crossing freight railway vehicles. The methodology involves the following steps: (i) data collection and pre-processing, (ii) feature extraction (iii) data fusion and (iv) feature discrimination. In the first phase, an FFT algorithm is applied to the acceleration track responses. Then, features are extracted after training a stacked sparse autoencoder, in which the main features of the responses are obtained after a compression stage using an encoder network. This lower dimensional layer forces the model to learn a compression of the input data. Then, these extracted features are merged using the Mahalanobis distance, which enhances the sensitivity to the damage recognition. Posteriorly, an outlier analysis is performed to distinguish a healthy wheel from a defective one and a cluster analysis to discriminate the two types of out-of-roundness (OOR) damage and classify the severity of each type of damage.

关键词： OOR wheel damage damage identification freight trains wayside condition monitoring stacked sparse autoencoder

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Fault Diagnosis for Power Batteries Based on a stacked sparse autoencoder and a Convolutional Block Attention Capsule Network

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PROCESSES 2024年第4期12卷 816页

作者： Zhou, Juan Zhang, Shun Wang, Peng China Jiliang Univ Coll Qual & Safety Engn Hangzhou 310018 Peoples R China China Automot Engn Res Inst Co Ltd Chongqing 401120 Peoples R China

The power battery constitutes the fundamental component of new energy vehicles. Rapid and accurate fault diagnosis of power batteries can effectively improve the safety and power performance of the vehicle. In response to the issues of limited generalization ability and suboptimal diagnostic accuracy observed in traditional power battery fault diagnosis models, this study proposes a fault diagnosis method utilizing a Convolutional Block Attention Capsule Network (CBAM-CapsNet) based on a stacked sparse autoencoder (SSAE). The reconstructed dataset is initially input into the SSAE model. Layer-by-layer greedy learning using unsupervised learning is employed, combining unsupervised learning methods with parameter updating and local fine-tuning to enhance visualization capabilities. The CBAM is then integrated into the CapsNet, which not only mitigates the effect of noise on the SSAE but also improves the model's ability to characterize power cell features, completing the fault diagnosis process. The experimental comparison results show that the proposed method can diagnose power battery failure modes with an accuracy of 96.86%, and various evaluation indexes are superior to CNN, CapsNet, CBAM-CapsNet, and other neural networks at accurately identifying fault types with higher diagnostic accuracy and robustness.

关键词： power battery stacked sparse autoencoder convolutional block attention capsule network fault diagnosis

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Research on Dung Beetle Optimization Based stacked sparse autoencoder for Network Situation Element Extraction

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IEEE ACCESS 2024年 12卷 24014-24026页

作者： Yang, Yongchao Zhao, Pan Chizhou Univ Sch Big Data & Artificial Intelligence Chizhou 247000 Peoples R China

Network security situation awareness enables networks to actively and effectively defend against network attacks, relying on the extraction of network situation elements as an initial and decisive step. In existing studies, the stacked sparse autoencoder (SSAE) has been employed to extract features from unlabeled network flows. However, obtaining the optimal hyperparameter combination is challenging due to its numerous hyperparameters. To address this issue, we propose a novel approach named DBO-SSAE that leverages dung beetle optimization (DBO) to select the optimal hyperparameters for SSAE automatically. Applied to the well-known UNSW-NB15 dataset, our model yields an optimal feature subset, which is evaluated across various binary classifiers with different metrics. Experimental results demonstrate that our approach improvesaccuracyandF1-measureby 0.2% to 1.5% while reducing the false negative rate(FNR)and false positive rate(FPR) by 0.06% to 7%, surpassing other feature extraction methods on the same classifier for the UNSW-NB15 dataset. Particularly, in conjunction with a lightweight bidirectional longs hort-term memory (BiLSTM), our model achieves metrics of 98.84%accuracy, 98.96%F1-measure, 1.86%FNR, and 0.6%FPR. This study could provide novel insights into the effective representation of network situation elements and lay the groundwork for a high-efficiency intrusion detection system

关键词： Feature extraction Principal component analysis Optimization Intrusion detection Telecommunication traffic Classification algorithms Support vector machines Encoding Network security Dung beetle optimization network security network situation element extraction stacked sparse autoencoder

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Six Phase Transmission Line Protection Using Bat Algorithm Tuned stacked sparse autoencoder

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年第2期51卷 113-130页

作者： Rao Althi, Tirupathi Koley, Ebha Ghosh, Subhojit Shukla, Sunil Kumar Natl Inst Technol Raipur Elect Engn Dept Raipur India Nirma Univ Inst Technol Dept Elect Engn Ahmadabad India MITS Gwalior Dept Informat Technol Gwalior India

Six-phase transmission lines have the capability to address the continually evolving power demand. It allows upgrading the power transfer capability of the prevailing three-phase double circuit line without major changes in the transmission corridors. However, the operational performance of any six-phase system is highly dependent on its protection scheme. The possibility of larger number of faults in six-phase system complicates the protection task. Furthermore, the harmonics intrusion arising because of nonlinear loading compromises the reliability of the conventional threshold-based protection schemes. In this regard, this article addresses the above-mentioned challenges by developing a protection scheme based on the hybrid frameworks of bat algorithm and stacked sparse autoencoder-deep neural network (SAE-DNN). To overcome the limitation of SAE-DNN regarding optimal selection of architecture and tuning parameters, the selection task has been formulated as an optimization problem and solved using Bat algorithm. The use of raw voltage and current signals as input to the SAE-DNN reduces the overall complexity of the protection scheme. The efficacy of the proposed scheme has been validated for all 120 types of faults under varying operating, loading and fault scenarios. Furthermore, the proposed scheme has been validated for practical settings by performing real-time simulations on OPAL-RT digital simulator.

关键词： bat algorithm deep neural network six phase transmission line protection stacked sparse autoencoder real time validation

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Web-S4AE: a semi-supervised stacked sparse autoencoder model for web robot detection

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NEURAL COMPUTING & APPLICATIONS 2023年第24期35卷 17883-17898页

作者： Jagat, Rikhi Ram Sisodia, Dilip Singh Singh, Pradeep Natl Inst Technol Raipur Dept Comp Sci & Engn Raipur 492010 India

Web robots are automated computer programs that can be exploited for benign and malicious activities such as website indexing, monitoring, or unauthorized content scraping and scalping. Several methods are available to detect automated web robots through their footprints and behaviors. Although the accuracy and efficiency of existing methods depend highly on the labeled web log data, countless web requests are generated daily with the help of web robots. Exhaustive and accurate manual labeling of reconstructed sessions is time-consuming and challenging. Further, effective detection of web robots is more challenging with unlabeled or partially labeled data. To address the aforementioned issues, we reformulated web robot detection as a semi-supervised learning problem. In this paper, we propose a deep learning-based Semi-Supervised stacked sparse autoencoder (Web-S4AE) for web robot detection. The proposed model uses content-based features and features extracted from web access log data to effectively classify web robots. The experiments were conducted on publicly available web log data from a library and information portal to assess the performance of Web-S4AE. The Web-S4AE model was trained in two phases. The first phase;comprises training the model with unlabeled data to extract the hidden information, and in the second phase, the model is fine-tuned using labeled data. The results suggest that incorporating more unlabeled data can significantly improve the classifier's performance. The Web-S4AE model's performance was also compared with other models such as the Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP).

关键词： Web robot detection Semi-supervised learning Machine learning Deep learning Deep feature extraction stacked sparse autoencoder

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Intrusion Detection Method Based on stacked sparse autoencoder and Sliced GRU for Connected Healthcare Systems

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第2期48卷 2061-2074页

作者： Gu, Zhaoyang Wang, Liangliang Li, Jinguo Wen, Mi Liu, Yuping Shanghai Univ Elect Power Coll Comp Technol & Sci Shanghai 201306 Peoples R China Nanjing Inst Technol Coll Comp Technol & Sci Nanjing 211100 Peoples R China

Connected healthcare systems face more and more cyber attacks recently. With the development of technology, people use intrusion detection systems (IDS) to detect network attacks and achieve effective results. The existing methods do not take into account the limited storage and computing power of wireless devices on connected healthcare systems. IDSs in the connected healthcare systems need to be real-time and lightweight. This paper proposes an IDS method based on stacked sparse autoencoder (sSAE) and sliced gated recurrent unit (SGRU). The sSAE reduces the dimensionality of the original traffic data and the memory required to calculate the covariance matrix. We slice the original data and input the processed data into the SGRU networks which are paralleled. Therefore, SGRU networks achieve real-time response. The method uses the AWID dataset. The experimental results show that our scheme is better than deep neural network (DNN), recurrent neural network (RNN), long short-term memory (LSTM) and other methods. Especially, the F1-score of the method is 2-5% higher than existing schemes, the detection time is 5-13 times shorter than other solutions, and the model size is smaller than the size of other models by at least 4 times.

关键词： Connected healthcare systems stacked sparse autoencoder Sliced GRU Intrusion detection

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Abnormal Condition Monitoring Based on stacked sparse autoencoder 1

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13th International Conference on Computer Engineering and Networks (CENet)

作者： Qiao, Lin Qu, Ruiting Song, Qing Liu, Biqi Liu, Xiaoqiang Liu, Yubo State Grid Liaoning Elect Power Co Ltd Informat & Commun Branch Shenyang Peoples R China

ISBN: (数字)9789819992430

ISBN: (纸本)9789819992454;9789819992430;9789819992423

In recent years, abnormal condition monitoring has provoked vast amount o attention and research from multiple disciplines. Especially in the microservice architecture based on the cloud platform, abnormal condition monitoring has emerged as a powerful instrument for improving the stability of service. However, along with the development of geometric growth in the number of microservice, due to the lack of systematic methods, it is difficult to effectively analyze and extract valuable information from the large amount of data collected, the abnormal monitoring in cloud platform O&M is facing new difficulties and challenges. Traditional operation and maintenance work mainly relies on human experience to analyze a large number of indicators to determine whether there is a failure, which is very inefficient and relies heavily on expert opinions. To address these problems, in this paper, we propose a abnormal condition monitoring method based on stacked sparse autoencoder for cloud platform. Our method aims to model the operating state based on the historical experience with deep autoencoder, then the constructed model is used to analyze the current status of the service and predict the possible abnormal condition. Experimental results on real-world datasets demonstrate the effectiveness of our method compared to the traditional methods.

关键词： Abnormal condition monitoring stacked sparse autoencoder Intelligent operation and maintenance

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stacked sparse autoencoder in cavitation noise signal data classification of hydro turbine based on power spectrum

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2020年第2期39卷 233-245页

作者： Kang, Ziyang Feng, Chi Wan, Xiaoqing Liu, Zhiliang Chen, Liwei Harbin Engn Univ Coll Informat & Commun Engn Harbin Heilongjiang Peoples R China Harbin Inst Large Elect Machinery Natl Engn Res Ctr Hydropower Equipment Harbin Peoples R China

Cavitation is a phenomenon that occurs during the continuous operation of a hydro turbine that directly affects the efficiency and working capacity of the unit. This paper proposes an innovative classification paradigm that uses deep learning-based methodologies in order to identify both cavitation noise signal and non-cavitation noise signal that will help prevent the damage or breakage in the earliest possible time to avoid more irreversible and irreparable damage to the hydro turbine. The stacked sparse autoencoder (SSA) nuclear framework is utilized to learn more abstract and invariant high-level features from the multiple feature sets. Then, the method on minimum redundancy maximum relevance (mRMR) selection is used to evaluate and sort out all the characteristics found by the stacked sparse autoencoder. Finally, the random forest (RF) classifier algorithm is employed to perform supervised fine-tuning and classification. The traditional supervised learning models such as support vector machine, logistic regression, and sparse representation classification are chosen to be used as contrast algorithms. SSA-mRMR-RF generally produces a better performance than the support vector machine, logistic regression and sparse representation classification when used in the same set of features. The SSA-mRMR-RF produced the highest overall average accuracy since it reached 93.18%. The SSA-mRMR-RF offers a 3.85% higher overall accuracy than the support vector machine. It also offers a 2.44% higher overall accuracy than logistic regression. In addition, it also offers a 20.30% higher overall accuracy than the sparse representation classification on the average. When the signals are divided into three categories, the highest overall accuracy decreased to 71.88%, and the classification accuracy of incipient noise signals is very low. Therefore, this paper proposes the models of power spectral-SSA-mRMR-RF and fast Fourier transform-SSA-mRMR-RF to be used, and these discoveries ar

关键词： Cavitation noise signal hydrophone stacked sparse autoencoder minimum redundancy maximum relevance random forest power spectrum

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Novel Pelican Optimization Algorithm (POA) With stacked sparse autoencoder (SSAE) Based IDS for Network Security

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2025年第5期36卷

作者： Kanimozhi, R. Madheswari, A. Neela Muthayammal Engn Coll Dept Comp Sci & Engn Rasipuram Tamil Nadu India Mahendra Engn Coll Fac Comp Sci & Engn Namakkal Tamil Nadu India

Security is a crucial factor for information systems and other vital infrastructures. Ensuring robust security measures is imperative due to the substantial volume of network traffic. On the other hand, many network components are susceptible to cyber threats and attacks due to their inherent properties. The increasing use of networks paves the way for widespread security vulnerabilities. In this context, the implementation of intrusion detection systems (IDS) plays a key role in safeguarding information systems and their network architectures. This research introduces an optimized deep learning model aimed at improving network security by accurately detecting intrusions. The proposed IDS, also termed as the POA-SSAE IDS model (pelican optimization model-stacked sparse autoencoder), integrates a POA for optimal feature selection and an SSAE for feature classification. The effectiveness of this IDS was tested using benchmark datasets, namely CICIDS2018 and KDDCUP'99. The results exhibited the proposed model's superior performance, achieving an accuracy of 97.45% on the CICIDS2018 dataset and 98.7% accuracy on the KDDCUP'99 dataset.

关键词： data privacy deep learning network security optimization pelican optimization algorithm stacked sparse autoencoder

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