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Wind turbine anomaly detection based on SCADA: A deep autoencoder enhanced by fault instances

ISA TRANSACTIONS 2023年第1期139卷 586-605页

作者： Liu, Jiarui Yang, Guotian Li, Xinli Wang, Qianming He, Yuchen Yang, Xiyun North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

An increasing number of deep autoencoder-based algorithms for intelligent condition monitoring and anomaly detection have been reported in recent years to improve wind turbine reliability. However, most existing studies have only focused on the precise modeling of normal data in an unsupervised manner;few studies have utilized the information of fault instances in the learning process, which results in suboptimal detection performance and low robustness. To this end, we first developed a deep autoencoder enhanced by fault instances, that is, a triplet-convolutional deep autoencoder (triplet-Conv DAE), jointly integrating a convolutional autoencoder and deep metric learning. Aided by fault instances, triplet-Conv DAE can not only capture normal operation data patterns but also acquire discriminative deep embedding features. Moreover, to overcome the difficulty of scarce fault instances, we adopted an improved generative adversarial network-based data augmentation method to generate high-quality synthetic fault instances. Finally, we validated the performance of the proposed anomaly detection method using a multitude of performance measures. The experimental results show that our method is superior to three other state-of-the-art methods. In addition, the proposed augmentation method can efficiently improve the performance of the triplet-Conv DAE when fault instances are insufficient. & COPY;2023 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Wind turbine Anomaly detection autoencoder Deep metric learning Data augmentation SCADA data

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TSMAE: A Novel Anomaly Detection Approach for Internet of Things Time Series Data Using Memory-Augmented autoencoder

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第5期10卷 2978-2990页

作者： Gao, Honghao Qiu, Binyang Barroso, Ramon J. Duran Hussain, Walayat Xu, Yueshen Wang, Xinheng Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Univ Valladolid Fac Telecommun Engn Valladolid 47002 Spain Victoria Univ Business Sch Melbourne Vic 3011 Australia Univ Technol Sydney Ultimo NSW 2007 Australia Xidian Univ Sch Comp Sci & Technol Xian 710126 Peoples R China Xian Jiaotong Liverpool Univ Sch Adv Technol Suzhou 215123 Peoples R China

With the development of communication, the Internet of Things (IoT) has been widely deployed and used in industrial manufacturing, intelligent transportation, and healthcare systems. The time-series feature of the IoT increases the data density and the data dimension, where anomaly detection is important to ensure hardware and software security. However, for the general anomaly detection methods, the anomaly may be well-reconstructed with tiny differences that are hard to discover. Measuring model complexity and the dataset feature space is a long and inefficient process. In this paper, we propose a memory-augmented autoencoder approach for detecting anomalies in IoT data, which is unsupervised, end-to-end, and not easily overgeneralized. First, a memory mechanism is introduced to suppress the generalization ability of the model, and a memory-augmented time-series autoencoder (TSMAE) is designed. Each memory item is encoded and recombined according to the similarity with the latent representation. Then, the new representation is decoded to generate the reconstructed sample, based on which the anomaly score can be obtained. Second, the addressing vector tends to be sparse by adding penalties and rectification functions to the loss. Memory modules are encouraged to extract typical normal patterns, thus inhibiting model generalization ability. Long short-term memory (LSTM) is introduced for decoding and encoding time-series data to obtain the contextual characteristics of time-series data. Finally, through experiments on the ECG and Wafer datasets, the validity of the TSMAE is verified. The rationality of the hyperparameter setting is discussed by visualizing the memory module addressing vector.

关键词： Anomaly detection Data models Task analysis Feature extraction Internet of Things Decoding Complexity theory time-series classification memory augmentation autoencoder

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Deep Self-Supervised Graph Attention Convolution autoencoder for Networks Clustering

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2023年第4期69卷 974-983页

作者： Chen, Chao Lu, Hu Hong, Haotian Wang, Hai Wan, Shaohua Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China

In recent years, graph-based deep learning algorithms have attracted widespread attention in the field of consumer electronics. Still, most of the current graph neural networks are based on supervised learning or semi-supervised learning, which often relies on the true labels of the given samples as auxiliary information. To solve this problem, we propose a Deep Self-Supervised Attention Convolution autoencoder Graph Clustering (DSAGC) model and use it for social networks clustering. We divide the proposed model into two parts: a pretext task and a downstream task. In the pretext task, we obtain pseudo-labels of samples by graph attention autoencoder and clustering, then adopt the proposed reliable sample selection mechanism to gain a high confidential sample. These samples and corresponding pseudo-labels are selected as input of downstream task for helping the downstream task to finish training. The model obtains the predictive labels of the sample from the downstream task. This model does not depend on the true label of the sample for training and uses the pseudo-labels produced by unsupervised learning to improve the clustering performance. We apply the proposed model to three commonly used public social network datasets to test its performance. Compared with the presented unsupervised clustering algorithms and other deep graph neural network algorithms, various metrics have shown that the proposed model consistently outperforms the state-of-the-art. The proposed method can be used for consumer behavior analysis. We release the source code at https://***/hulu88/DSAGC.

关键词： Self-supervised autoencoder reliable sample selection social networks clustering

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Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第7期17卷 3535-3543页

作者： Abedi, Ali S. Khan, Shehroz Univ Hlth Network KITE Toronto ON Canada

Student engagement is an important factor in meeting the goals of virtual learning programs. Automatic measurement of student engagement provides helpful information for instructors to meet learning program objectives and individualize program delivery. Many existing approaches solve video-based engagement measurement using the traditional frameworks of binary classification (classifying video snippets into engaged or disengaged classes), multi-class classification (classifying video snippets into multiple classes corresponding to different levels of engagement), or regression (estimating a continuous value corresponding to the level of engagement). However, we observe that while the engagement behavior is mostly well defined (e.g., focused, not distracted), disengagement can be expressed in various ways. In addition, in some cases, the data for disengaged classes may not be sufficient to train generalizable binary or multi-class classifiers. To handle this situation, in this paper, for the first time, we formulate detecting disengagement in virtual learning as an anomaly detection problem. We design various autoencoders, including temporal convolutional network autoencoder, long short-term memory autoencoder, and feedforward autoencoder using different behavioral and affect features for video-based student disengagement detection. The result of our experiments on two publicly available student engagement datasets, DAiSEE and EmotiW, shows the superiority of the proposed approach for disengagement detection as an anomaly compared to binary classifiers for classifying videos into engaged versus disengaged classes (with an average improvement of 9% on the area under the curve of the receiver operating characteristic curve and 22% on the area under the curve of the precision-recall curve).

关键词： Student engagement Disengagement detection Affect states autoencoder Anomaly detection TCN Temporal convolutional autoencoder

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Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2023年第6期31卷 1113-1128页

作者： Kiraz, Ahmet Hidayet Djibrillah, Fatime Oumar Yuksel, Mehmet Emin Erciyes Univ Grad Sch Nat & Appl Sci Dept Biomed Engn Kayseri Turkiye Erciyes Univ Fac Engn Dept Biomed Engn Kayseri Turkiye

Accurate analysis and classification of medical images are essential factors in clinical decision-making and patient care. A novel comparative approach for medical image classification is proposed in this study. This new approach involves several steps: deep feature extraction, which extracts the informative features from medical images;concatenation, which concatenates the extracted deep features to form a robust feature vector;dimensionality reduction with autoencoder, which reduces the dimensionality of the feature vector by transforming it into a different feature space with a lower dimension;and finally, these features obtained from all these steps were fed into multiple machine learning classifiers (SVM, KNN, linear DA, and ANN) for the classification purpose. The study is performed to conduct a comparative analysis, aiming to evaluate the individual impact of each step within the proposed methodology and also assess the performance of each implemented classifier in order to find a best pipeline for medical image classification. The effectiveness of the proposed approach is assessed using two different medical image datasets. The performance assessment for the classifiers implemented is achieved using overall accuracy, sensitivity, and specificity metrics. The findings show that the linear DA classifier preceded by deep feature extraction, concatenation, and dimensionality reduction reveals itself to be a very efficient pipeline for accurate classification of medical images by utilizing a very small number of features.

关键词： Medical images classification deep feature extraction concatenation dimensionality reduction autoencoder

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Solar Irradiation Prediction Hybrid Framework Using Regularized Convolutional BiLSTM-Based autoencoder Approach

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IEEE ACCESS 2023年 11卷 131362-131375页

作者： Chiranjeevi, Madderla Karlamangal, Skandha Moger, Tukaram Jena, Debashisha Natl Inst Technol Karnataka Dept Elect & Elect Engn Mangalore 575025 India

Solar irradiance prediction is an essential subject in renewable energy generation. Prediction enhances the planning and management of solar installations and provides several economic benefits to energy companies. Solar irradiation, being highly volatile and unpredictable makes the forecasting task complex and difficult. To address the shortcomings of the traditional approaches, this research developed a hybrid resilient architecture for an enhanced solar irradiation forecast by employing a long short-term memory (LSTM) autoencoder, convolutional neural network (CNN), and the Bi-directional Long Short Term Memory (BiLSTM) model with grid search optimization. The suggested hybrid technique is comprised of two parts: feature encoding and dimensionality reduction using an LSTM autoencoder, followed by a regularized convolutional BiLSTM. The encoder is tasked with extracting the key features in order to deduce the input into a compact latent representation. The decoder network then predicts solar irradiance by analyzing the encoded representation's attributes. The experiments are conducted on three publicly available data sets collected from Desert Knowledge Australia Solar Centre (DKASC), National Solar Radiation Database (NSRDB), and Hawaii Space Exploration Analog and Simulation (HI-SEAS) Habitat. The analysis of univariate and multivariate-multi step ahead forecasting performed independently and it is compared with the conventional approaches. Several benchmark forecasting models and three performance metrics are utilized to validate the hybrid approach's prediction performance. The results show that the proposed architecture outperforms benchmark models in accuracy.

关键词： autoencoder BiLSTM convolution neural network forecasting solar power generation

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Diagnosis of malaria disease by integrating chi-square feature selection algorithm with convolutional neural networks and autoencoder network

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2023年第5期45卷 975-985页

作者： caliskan, Abidin Batman Univ Fac Engn & Architecture Dept Comp Engn Batman Turkiye Batman Univ Fac Engn & Architecture Dept Comp Engn TR-72060 Batman Turkiye

Malaria is a febrile illness caused by a parasite called plasmodium. This life-threatening disease is preventable and treatable if diagnosed early. The World Health Organization aims to reduce the global malaria incidence and death rates by at least 90% until 2030. This disease is diagnosed by visually analyzing red blood cells with a microscope by experienced radiologists. Therefore, this situation may be erroneous due to subjective interpretations. In this study, red blood cells were trained with deep learning-based convolutional neural networks to diagnose malaria, and thus, their deep features were obtained. These obtained features are also trained with autoencoder networks. Thus, the chi-square feature selection algorithm was used to obtain distinctive features. Finally, the unique feature set obtained is given as an introduction to machine learning algorithms, and then a unique diagnostic model is proposed. As a result, 100% accuracy rate was obtained. The results are promising for the diagnosis of malaria disease.

关键词： Malaria convolutional neural network autoencoder chi-square feature selection machine learning classification

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Calibrated reconstruction based adversarial autoencoder model for novelty detection

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PATTERN RECOGNITION LETTERS 2023年第1期169卷 50-57页

作者： Huang, Yi Li, Ying Jourjon, Guillaume Seneviratne, Suranga Thilakarathna, Kanchana Cheng, Adriel Webb, Darren Xu, Richard Yi Da Univ Technol Sydney 15 Broadway Ultimo NSW 2007 Australia Data61 CSIRO 13 Garden St Eveleigh NSW 2015 Australia Univ Sydney City Rd Camperdown NSW 2006 Australia Def Sci & Technol Grp Third Ave Edinburgh SA 5111 Australia

Novelty detection detects outliers located at any location, such as abnormalities (i.e., far distance outliers) and novel/unobserved patterns (i.e., close distance outliers). While many novelty detection approaches have been proposed in the literature, they generally focus on detecting one specific type of outlier, e.g., Multi-Class Open Set Recognition (MCOSR) and One-Class Novelty Detection (OCND) approaches are ap-plied for far and close distance outlier detection, respectively. However, in practice, it is difficult to mea-sure in advance whether the distance between outliers and inliers is far or close. Recent work on outlier detection at any location with a unified model has yielded mixed performance. In this paper, we pro-pose a new unified model, named Calibrated Reconstruction Based Adversarial autoencoder (CRAAE), for location agnostic outlier detection. The key idea is to integrate implicit and explicit confidence calibra-tion strategies into a reconstruction based model for building a more accurate decision boundary. We leverage the category information disentangled from feature space to calibrate the decision metric (i.e., reconstruction error) constructed in the original data space. CRAAE also adds Uniform or Dirichlet noise into the artificial outlier generation process to represent various outliers. Experimental results show that CRAAE can outperform state-of-the-art unified models (e.g., GPND) and achieve similar performance with OCND and MCOSR methods in close and far distance outlier detection, respectively.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Novelty detection Reconstruction autoencoder Calibration

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Adaptation of autoencoder for Sparsity Reduction From Clinical Notes Representation Learning

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IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023年 11卷 469-478页

作者： Le, Thanh-Dung Noumeir, Rita Rambaud, Jerome Sans, Guillaume Jouvet, Philippe Univ Quebec Ecole Technol Super Biomed Informat Proc Lab Montreal PQ H3C 1K3 Canada Univ Montreal CHU St Justine Res Ctr Montreal PQ H3T 1J4 Canada

When dealing with clinical text classification on a small dataset, recent studies have confirmed that a well-tuned multilayer perceptron outperforms other generative classifiers, including deep learning ones. To increase the performance of the neural network classifier, feature selection for the learning representation can effectively be used. However, most feature selection methods only estimate the degree of linear dependency between variables and select the best features based on univariate statistical tests. Furthermore, the sparsity of the feature space involved in the learning representation is ignored. Goal: Our aim is, therefore, to access an alternative approach to tackle the sparsity by compressing the clinical representation feature space, where limited French clinical notes can also be dealt with effectively. Methods: This study proposed an autoencoder learning algorithm to take advantage of sparsity reduction in clinical note representation. The motivation was to determine how to compress sparse, high-dimensional data by reducing the dimension of the clinical note representation feature space. The classification performance of the classifiers was then evaluated in the trained and compressed feature space. Results: The proposed approach provided overall performance gains of up to 3% for each test set evaluation. Finally, the classifier achieved 92% accuracy, 91% recall, 91% precision, and 91% f1-score in detecting the patient's condition. Furthermore, the compression working mechanism and the autoencoder prediction process were demonstrated by applying the theoretic information bottleneck framework. Clinical and Translational Impact Statement- An autoencoder learning algorithm effectively tackles the problem of sparsity in the representation feature space from a small clinical narrative dataset. Significantly, it can learn the best representation of the training data because of its lossless compression capacity compared to other approaches. Consequently,

关键词： Clinical natural language processing cardiac failure autoencoder sparsity

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DAS-GNN: Denoising autoencoder integrated with self-supervised learning in graph neural network-based recommendations

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APPLIED INTELLIGENCE 2023年第14期53卷 17292-17309页

作者： Dai, Jiuqian Yuan, Weihua Bao, Chen Zhang, Zhijun Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250001 Shandong Peoples R China

To enhance the recommendation performance, session-based recommendations typically model based on graph neural networks (GNN). These models use the most recently clicked item as the user's short-term interest, as well as the query vector in the attention mechanism. Based on it, the attention score is calculated with the remaining items to obtain the user's long-term interest. However, the obtained representation of long-term interest is one-sided. Furthermore, unlike other recommendation technology, such as collaborative filtering that includes the user's entire history information, the session-based recommendation is more vulnerable to data sparsity. Existing models primarily make predictions based on observable user-item interactions and ignore items not interacted with by users. To address the aforementioned issues, we propose the denoising autoencoder integrated with self-supervised learning (SSL) in graph neural networks (DAS-GNN). In DAS-GNN, the query extraction module based on denoising autoencoder can mine multiple user interests and assist long-term interest to express user needs more comprehensively. We propose an effective way of dividing positive and negative samples in the SSL module and use adaptive thresholds to mine negative hard samples, thereby improving training efficiency and alleviating data sparsity. Extensive experiments demonstrate that the proposed DAS-GNN outperforms state-of-the-art models on four benchmarks. The source code is available at: https://***/daijiuqian/DAS-GNN.

关键词： Session-based recommendation autoencoder Graph neural networks Attention Self-supervised learning

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