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Health Indicator for Low-Speed Axial Bearings Using variational autoencoders

IEEE ACCESS 2020年 8卷 35842-35852页

作者： Hemmer, Martin Klausen, Andreas Huynh Van Khang Robbersmyr, Kjell G. Waag, Tor, I Univ Agder Dept Engn Sci N-4877 Grimstad Norway MHWirth AS N-4639 Kristiansand Norway NORCE Norwegian Res Ctr AS N-4877 Grimstad Norway

This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using variational autoencoders (VAEs), trained on each speed reference in the dataset. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the suggested methodology allows for setting the probability of false alarms when encoding new data points to the latent variable space using the trained model. The effectiveness of the proposed method is validated based on two different datasets: from a workshop test of an offshore drilling machine and from an in-house test rig for axial bearings. In both datasets, the HI is exceeding the warning and alarm levels with a probability of false alarm (PFA) of 10(-6), and the method is most effective at lower shaft speeds.

关键词： Training Decoding Dimensionality reduction Drilling machines Fault detection Industries Rolling bearings Bearing fault detection condition monitoring unsupervised learning variational autoencoder conditional variational autoencoder generative models

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An entangled mixture of variational autoencoders approach to deep clustering

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NEUROCOMPUTING 2023年第1期529卷 182-189页

作者： Caciularu, Avi Goldberger, Jacob Bar Ilan Univ Comp Sci Dept Ramat Gan Israel Bar Ilan Univ Engn Fac Ramat Gan Israel

We present a novel deep clustering algorithm that utilizes a variational autoencoder (VAE) framework with an entangled multi encoder-decoder neural architecture. Our model enforces a complementary structure that guides the learned latent representations towards a better space arrangement. It differs from previous VAE-based clustering algorithms by employing a new generative model that uses multiple encoder-decoders that are entangled to provide a joint clustering decision. The optimal clustering is found by optimizing a lower bound of the model likelihood function. Both the reconstruction component and the regularization component of the ELBO objective function are explicitly involved in the clustering procedure. We show that this modeling results in both better clustering capabilities and improved data generation. The proposed method is evaluated on standard datasets and is shown to significantly outper-form state-of-the-art deep clustering methods.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Deep clustering variational autoencoder Mixture model

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Discovering web services in social web service repositories using deep variational autoencoders

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INFORMATION PROCESSING & MANAGEMENT 2020年第4期57卷 102231-102231页

作者： Lizarralde, Ignacio Mateos, Cristian Zunino, Alejandro Majchrzak, Tim A. Gronli, Tor-Morten UNICEN CONICET Tandil ISISTAN Buenos Aires DF Argentina Univ Agder Kristiansand Norway Kristiania Univ Coll Oslo Norway

Web Service registries have progressively evolved to social networks-like software repositories. Users cooperate to produce an ever-growing, rich source of Web APIs upon which new value-added Web applications can be built. Such users often interact in order to follow, comment on, consume and compose services published by other users. In this context, Web Service discovery is a core functionality of modern registries as needed Web Services must be discovered before being consumed or composed. Many efforts to provide effective keyword-based service discovery mechanisms are based on Information Retrieval techniques as services are described using structured or unstructured textdocuments that specify the provided functionality. However, traditional techniques suffer from term-mismatch, which means that only the terms that are contained in both user queries and descriptions are exploited to perform service retrieval. Early feature learning techniques such as LSA or LDA tried to solve this problem by finding hidden or latent features in text documents. Recently, alternative feature learning based techniques such as Word Embeddings achieved state of the art results for Web Service discovery. In this paper, we propose to learn features from service descriptions by using variational autoencoders, a special kind of autoencoder which restricts the encoded representation to model latent variables. autoencoders in turn are deep neural networks used for unsupervised learning of efficient codings. We train our autoencoder using a real 17 113-service dataset extracted from the *** API social repository. We measure discovery efficacy by using both Recall and Precision metrics, achieving significant gains compared to both Word Embeddings and classic latent features modelling techniques. Also, performance-oriented experiments show that the proposed approach can be readily exploited in practice.

关键词： Service-oriented computing Web Services Service discovery Deep neural network variational autoencoder

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Analog ensemble data assimilation and a method for constructing analogs with variational autoencoders

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QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 2021年第734期147卷 139-149页

作者： Grooms, Ian Univ Colorado Dept Appl Math Boulder CO 80309 USA

It is proposed to use analogs of the forecast mean to generate an ensemble of perturbations for use in ensemble optimal interpolation (EnOI) or ensemble variational (EnVar) methods. A new method of constructing analogs using variational autoencoders (VAEs;a machine learning method) is proposed. The resulting analog methods using analogs from a catalog (AnEnOI), and using constructed analogs (cAnEnOI), are tested in the context of a multiscale Lorenz-'96 model, with standard EnOI and an ensemble square root filter for comparison. The use of analogs from a modestly-sized catalog is shown to improve the performance of EnOI, with limited marginal improvements resulting from increases in the catalog size. The method using constructed analogs (cAnEnOI) is found to perform as well as a full ensemble square root filter, and to be robust over a wide range of tuning parameters.

关键词： analogs Ensemble Optimal Interpolation machine learning variational autoencoder

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Hybrid Unsupervised Time-Series Anomaly Detection for Industrial IoT Based on Spatio-Temporal Feature 18th

Hybrid Unsupervised Time-Series Anomaly Detection for Indus...

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18th China Conference on Wireless Sensor Networks, CWSN 2024

作者： Dong, Yu Yao, Muyan Qi, Peng Tao, Dan School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819621880

The rapid development of the Industrial Internet of Things (IIoT) has enabled the communication of numerous devices, resulting in the generation of massive time-series data. The goal of time-series anomaly detection algorithms is to quickly detect anomalous behavior and trigger warnings. We propose a Hybrid Unsupervised Time-series Anomaly Detection method (HUTAD) that fully leverages two different anomaly detection strategies: spatial feature extraction by a variational autoencoder and temporal feature extraction by a variational autoencoder and a recurrent feature extraction LSTM structure with predictive capability. Through a dynamic fusion module based on the self-attention mechanism, we integrate the two methods to obtain the output, analyze the spatio-temporal patterns of the data, and detect potential anomalies. We incorporate adversarial training in our model to further improve its accuracy and robustness. Finally, we use three widely-used large-scale datasets to validate our model. The experimental results show that our method outperforms the baseline methods in F1-Score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial training Anomaly detection Industrial loT Self-attention variational autoencoder

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Joint variational autoencoders for multimodal imputation and embedding

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NATURE MACHINE INTELLIGENCE 2023年第6期5卷 631-+页

作者： Kalafut, Noah Cohen Huang, Xiang Wang, Daifeng Univ Wisconsin Madison Dept Comp Sci Madison WI 53706 USA Univ Wisconsin Madison Waisman Ctr Madison WI 53706 USA Univ Wisconsin Madison Dept Biostat & Med Informat Madison WI 53706 USA

While single-cell multimodal datasets allow for the measurement of individual cells to understand cellular and molecular mechanisms, generating multimodal data for many cells is costly and challenging. Cohen Kalafut and colleagues develop a machine learning model capable of imputing single-cell modalities and prioritizing multimodal features, such as gene expression, chromatin accessibility and electrophysiology. Single-cell multimodal datasets have measured various characteristics of individual cells, enabling a deep understanding of cellular and molecular mechanisms. However, multimodal data generation remains costly and challenging, and missing modalities happen frequently. Recently, machine learning approaches have been developed for data imputation but typically require fully matched multimodalities to learn common latent embeddings that potentially lack modality specificity. To address these issues, we developed an open-source machine learning model, Joint variational autoencoders for multimodal Imputation and Embedding (JAMIE). JAMIE takes single-cell multimodal data that can have partially matched samples across modalities. variational autoencoders learn the latent embeddings of each modality. Then, embeddings from matched samples across modalities are aggregated to identify joint cross-modal latent embeddings before reconstruction. To perform cross-modal imputation, the latent embeddings of one modality can be used with the decoder of the other modality. For interpretability, Shapley values are used to prioritize input features for cross-modal imputation and known sample labels. We applied JAMIE to both simulation data and emerging single-cell multimodal data including gene expression, chromatin accessibility, and electrophysiology in human and mouse brains. JAMIE significantly outperforms existing state-of-the-art methods in general and prioritized multimodal features for imputation, providing potentially novel mechanistic insights at cellular resolution.

关键词： Data imputation Data integration Deep learning Multimodal Single-cell variational autoencoder

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Utilizing variational autoencoders in the Bayesian Inverse Problem of Photoacoustic Tomography

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SIAM JOURNAL ON IMAGING SCIENCES 2023年第1期16卷 89-110页

作者： Sahlstrom, Teemu Tarvainen, Tanja Univ Eastern Finland Dept Appl Phys Kuopio Finland UCL Dept Comp Sci London England

There has been an increasing interest in utilizing machine learning methods in inverse problems and imaging. Most of the work has, however, concentrated on image reconstruction problems, and the number of studies regarding the full solution of the inverse problem is limited. In this work, we study a machine learning--based approach for the Bayesian inverse problem of photoacoustic tomography. We develop an approach for estimating the posterior distribution in photoacoustic tomography using an approach based on the variational autoencoder. The approach is evaluated with numerical simulations and compared to the solution of the inverse problem using a Bayesian approach.

关键词： photoacoustic tomography Bayesian inverse problems variational Bayesian methods machine learning uncertainty quantification variational autoencoder

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Extracting a biologically latent space of lung cancer epigenetics with variational autoencoders

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BMC BIOINFORMATICS 2019年第18期20卷 1-7页

作者： Wang, Zhenxing Wang, Yadong Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China

Background Lung cancer is one of the most malignant tumors, causing over 1,000,000 deaths each year worldwide. Deep learning has brought success in many domains in recent years. DNA methylation, an epigenetic factor, is used for model training in many studies. There is an opportunity for deep learning methods to analyze the lung cancer epigenetic data to determine their subtypes for appropriate treatment. Results Here, we employ variational autoencoders (VAEs), an unsupervised deep learning framework, on 450K DNA methylation data of TCGA-LUAD and TCGA-LUSC to learn latent representations of the DNA methylation landscape. We extract a biologically relevant latent space of LUAD and LUSC samples. It is showed that the bivariate classifiers on the further compressed latent features could classify the subtypes accurately. Through clustering of methylation-based latent space features, we demonstrate that the VAEs can capture differential methylation patterns about subtypes of lung cancer. Conclusions VAEs can distinguish the original subtypes from manually mixed methylation data frame with the encoded features of latent space. Further applications about VAEs should focus on fine-grained subtypes identification for precision medicine.

关键词： DNA methylation Lung cancer variational autoencoder

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Partial Multiple Imputation With variational autoencoders: Tackling Not at Randomness in Healthcare Data

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第8期26卷 4218-4227页

作者： Pereira, Ricardo Cardoso Abreu, Pedro Henriques Rodrigues, Pedro Pereira Univ Coimbra Ctr Informat & Syst Univ Coimbra Dept Informat Engn P-3030290 Coimbra Portugal Univ Porto Fac Med MEDCIDS Ctr Hlth Technol & Serv Res P-4200319 Porto Portugal

Missing data can pose severe consequences in critical contexts, such as clinical research based on routinely collected healthcare data. This issue is usually handled with imputation strategies, but these tend to produce poor and biased results under the Missing Not At Random (MNAR) mechanism. A recent trend that has been showing promising results for MNAR is the use of generative models, particularly variational autoencoders. However, they have a limitation: the imputed values are the result of a single sample, which can be biased. To tackle it, an extension to the variational autoencoder that uses a partial multiple imputation procedure is introduced in this work. The proposed method was compared to 8 state-of-the-art imputation strategies, in an experimental setup with 34 datasets from the medical context, injected with the MNAR mechanism (10% to 80% rates). The results were evaluated through the Mean Absolute Error, with the new method being the overall best in 71% of the datasets, significantly outperforming the remaining ones, particularly for high missing rates. Finally, a case study of a classification task with heart failure data was also conducted, where this method induced improvements in 50% of the classifiers.

关键词： Medical services Task analysis Data models Principal component analysis Neural networks Mathematical models Mice Healthcare data missing data missing not at random partial multiple imputation variational autoencoder

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Aspect-level item recommendation based on user reviews with variational autoencoders

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INFORMATION SCIENCES 2024年 671卷

作者： Ou, Wei Huynh, Van-Nam Zhejiang Gongshang Univ Sch Tourism & Urban Rural Planning Hangzhou Peoples R China Japan Adv Inst Sci & Technol Sch Knowledge Sci Nomi Japan

In this paper we propose an aspect-based recommendation model based on variational autoencoders, that provides not only coarse predictions about what items users may like, but also finer-grained predictions of specific aspects users may be interested in of the recommended items. The proposed model first employs convolution operations to learn probability distributions of aspect-level embeddings for users and items from user aspect-level sentiments on seen items. Then it samples from these distributions and feeds the samples into transpose convolution operations to 'reconstruct' missing aspect-level sentiments for unseen items, thereby serving as signals for generating recommendations. To prevent overfitting, we impose a prior on the representation distributions and penalize the model if the learned distributions diverge from the prior. Based on the output of the proposed model, we propose a two-stage ranking scheme that combines aspect-level and overall sentiment signals to rank items. Experiment results show that the proposed model outperforms state -of -the -art aspect-based recommendation models, and the two-stage ranking scheme improves the traditional ranking by overall sentiment predictions.

关键词： Aspect-based recommender systems variational autoencoder Item ranking Aspect-level sentiment prediction

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