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Failure Detection in Sensors via variational autoencoders and Image-Based Feature Representation

SENSORS 2025年第7期25卷 2175-2175页

作者： Moreno Haro, Luis Miguel Oliveira-Filho, Adaiton Agard, Bruno Tahan, Antoine Polytech Montreal Dept Math & Ind Engn Lab Intelligence Donnees CIRRELT Montreal PQ H3T 0A3 Canada Ecole Technol Super Dept Mech Engn Montreal PQ H3C 1K3 Canada

This paper presents a novel approach for detecting sensor failures using image-based feature representation and the Convolutional variational autoencoder (CVAE) model. Existing methods are limited when analyzing multiple failure modes simultaneously or adapting to diverse sensor data. This limitation may compromise decision-making and system performance, hence the need for more flexible and resilient models. The proposed approach transforms sensor data into image-based feature representations of statistics such as mean, variance, kurtosis, entropy, skewness, and correlation. The CVAE is trained on such image representations, and the corresponding reconstruction error leads to a Health Index (HI) for detecting multiple sensor failures. Moreover, the CVAE latent space is used to define a complementary HI and a convenient visualization tool, enhancing the interpretability of the proposed approach. The evaluation of the proposed detection approach with data comprising diverse configurations of faulty sensors showed encouraging results. The proposed approach is illustrated in an industrial case study emerging from the aeronautical domain, with data from a complex electromechanical system comprising nearly 80 sensor measurements at a 1 Hz sampling rate. The results demonstrate the potential of the proposed method in detecting multiple sensor failures.

关键词： sensor failure detection health index variational autoencoder feature representation aeronautical sensors

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A few-shot object detection method for garbage via variational autoencoders and feature aggregation

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WASTE MANAGEMENT 2025年 200卷 114754页

作者： Xue, Shuya Song, Dian Chen, Wei Zhao, Lei Zhou, Qian Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China Soochow Univ Sch Polit Sci & Publ Adm Suzhou Peoples R China

Outdoor waste detection plays a pivotal role in environmental monitoring and waste management systems. Traditional garbage detectors rely heavily on a large amount of labelled data for training. However, these approaches are resource-intensive and ill-suited for waste categories that evolve quickly or are uncommon. To address the limitation, we propose a novel few-shot object detection (FSOD) method, named Few-Shot Garbage Detection (FSGD), which is tailored to identify garbage with limited labelled data. In the context of garbage detection, the changes in waste shapes caused by human behaviours can result in situations where the support images fail to fully represent category information. To tackle the issue, we utilize variational autoencoders (VAEs) to infer class distributions and sample robust variational features, ensuring an accurate representation of the garbage categories. Moreover, we propose an advanced aggregation strategy to establish correlations between support and query features. This strategy addresses the common problem in FSOD where the Region Proposal Network (RPN) is insensitive to novel categories. Additionally, we separate the weight of backbone network shared by support and query branches, which improves performance in a simple yet efficient way. Extensive experiments demonstrate that our method outperforms existing state-of-the-art FSOD methods in all evaluated scenarios on garbage detection datasets. Furthermore, we evaluate the generalization ability of the proposed FSGD approach on the publicly available Pascal VOC dataset, and the results indicate that FSGD also performs better than compared methods on this dataset.

关键词： Garbage Detection Few-shot Object Detection Waste localization Waste recognition variational autoencoder Feature Aggregation

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Multi-head attention-based variational autoencoders ensemble for remaining useful life prediction of aero-engines

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第1期36卷

作者： Wang, Yuxiao Suo, Chao Zhao, Yuyu Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China

Accurate remaining useful life (RUL) prediction of aero-engines through condition monitoring (CM) data is of great significance for flight reliability and safety. Although deep learning (DL)-based approaches have been widely considered, individual DL models suffer from significant stochasticity and limited generalizability when predicting the RUL. To solve this issue, a novel multi-head attention-based variational autoencoders (MHAT-VAEs) ensemble model is proposed. Two distinct MHAT-VAEs are designed, employing linear and convolutional operations to capture global and temporal compressed representations of the CM data. Additionally, a dual-level ensemble strategy is introduced to adaptively fuse the outputs of the two base learners. A hyperparameter optimization method is also implemented to further enhance the efficiency and performance of the base learners. The effectiveness of the proposed method is validated using the C-MAPSS and N-CMAPSS datasets, with experimental results showing that it outperforms state-of-the-art approaches.

关键词： Remaining useful life multi-head attention mechanism variational autoencoder ensemble learning

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Machine Learning-Driven Discovery of Thermoset Shape Memory Polymers With High Glass Transition Temperature Using variational autoencoders

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JOURNAL OF POLYMER SCIENCE 2025年第5期63卷 1095-1107页

作者： Teimouri, Amir Li, Guoqiang Louisiana State Univ Dept Mech & Ind Engn Baton Rouge LA 70803 USA

The discovery of high-performance shape memory polymers (SMPs) with enhanced glass transition temperatures (Tg) is of paramount importance in fields such as geothermal energy, oil and gas, aerospace, and other high-temperature applications, where materials are required to exhibit shape memory effect at extremely high-temperature conditions. Here, we employ a novel machine learning framework that integrates transfer learning with variational autoencoders (VAE) to efficiently explore the chemical design space of SMPs and identify new candidates with high Tg values. We systematically investigate the effect of different latent space dimensions on the VAE model performance. Several machine learning models are then trained to predict Tg. We find that the SVM model demonstrates the highest predictive accuracy, with R2 values exceeding 0.87 and a mean absolute percentage error as low as 6.43% on the test set. Through systematic molar ratio adjustments and VAE-based fingerprinting, we discover novel SMP candidates with Tg values between 190 degrees C and 200 degrees C, suitable for high-temperature applications. These findings underscore the effectiveness of combining VAEs and SVM for SMP discovery, offering a scalable and efficient method for identifying polymers with tailored thermal properties.

关键词： latent space optimization polymer discovery shape memory polymer transfer learning variational autoencoder

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Identifying Brain Ageing Trajectories Using variational autoencoders with Regression Model in Neuroimaging Data Stratified by Sex and Validated Against Dementia-Related Risk Factors 7th

Identifying Brain Ageing Trajectories Using Variational Auto...

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19th International Workshop on Predictive Intelligence in Medicine

作者： Calm Salvans, Berta Cumplido Mayoral, Irene Domingo Gispert, Juan Vilaplana, Veronica Pasqual Maragall Fdn Barcelona Beta Brain Res Ctr Barcelona Spain Univ Politecn Cataluna Dept Signal Theory & Commun Barcelona Spain

ISBN: (纸本)9783031745607;9783031745614

Neuroimaging-derived brain age has been identified as a promising biomarker for accelerated brain age;however, the ageing process is highly heterogeneous and there is a need to further study the different brain ageing trajectories. In this study, we implemented a variational autoencoder (VAE) based model coupled with regression to identify different age-related patterns. Additionally, we correlated the patterns obtained, using a linear regression approach, with dementia-related risk factors. The model was evaluated in different cohorts, UK Biobank and ALFA+, to assess the robustness of the approach. The results showed a feasible strategy for detecting and validating brain age-related trajectories to identify possible early deviations using morphological brain data.

关键词： Neurodegenerative disease Brain age model Ageing trajectories variational autoencoder Latent space disentanglement

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Addressing Pilot Contamination in Channel Estimation with variational autoencoders

Addressing Pilot Contamination in Channel Estimation with Va...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Kasibovic, Amar Fesl, Benedikt Baur, Michael Utschick, Wolfgang TUM School of Computation Information and Technology Technical University of Munich Munich Germany

ISBN: (纸本)9798350368741

Pilot contamination (PC) is a well-known problem that affects massive multiple-input multiple-output (MIMO) systems. When frequency and pilots are reused between different cells, PC constitutes one of the main bottlenecks of the system's performance. In this paper, we propose a method based on the variational autoencoder (VAE), capable of reducing the impact of PC-related interference during channel estimation (CE). We obtain the first and second-order statistics of the conditionally Gaussian (CG) channels for both the user equipments (UEs) in a cell of interest and those in interfering cells, and we then use these moments to compute conditional linear minimum mean square error estimates. We show that the proposed estimator is capable of exploiting the interferers' additional statistical knowledge, outperforming other classical approaches. Moreover, we highlight how the achievable performance is tied to the chosen setup, making the setup selection crucial in the study of multi-cell CE. © 2025 IEEE.

关键词： channel estimation generative model pilot contamination variational autoencoder

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Enabling Feedback-Free MIMO Transmission for FD-RAN: A Data-Driven Approach

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第3期24卷 2437-2454页

作者： Liu, Jingbo Chen, Jiacheng Liu, Zongxi Zhou, Haibo Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Pengcheng Lab Shenzhen 518000 Peoples R China Pengcheng Lab Dept Strateg & Adv Interdisciplinary Res Shenzhen 518000 Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China

To enhance flexibility and facilitate resource cooperation, a novel fully-decoupled radio access network (FD-RAN) architecture is proposed for 6G. However, the decoupling of uplink (UL) and downlink (DL) in FD-RAN makes the existing feedback mechanism ineffective. To this end, we propose an end-to-end data-driven MIMO solution without the conventional channel feedback procedure. Data-driven MIMO can alleviate the drawbacks of feedback including overheads and delay, and can provide customized precoding design for different BSs based on their historical channel data. It essentially learns a mapping from geolocation to MIMO transmission parameters. We first present a codebook-based approach, which selects transmission parameters from the statistics of discrete channel state information (CSI) values and utilizes nearest neighbor interpolation for spatial inference. We further present a non-codebook-based approach, which 1) derives the optimal precoder from the singular value decomposition (SVD) of the channel;2) utilizes variational autoencoder (VAE) to select the representative precoder from the latent Gaussian representations;and 3) exploits Gaussian process regression (GPR) to predict unknown precoders in the space domain. Extensive simulations are performed on a link-level 5G simulator using realistic ray-tracing channel data. The results demonstrate the effectiveness of data-driven MIMO, showcasing its potential for application in FD-RAN and 6G.

关键词： 6G fully-decoupled RAN MIMO codebook variational autoencoder 6G fully-decoupled RAN MIMO codebook variational autoencoder

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Unsupervised deep learning approach for structural anomaly detection using probabilistic features

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2025年第1期24卷 3-33页

作者： Wan, Hua-Ping Zhu, Yi-Kai Luo, Yaozhi Todd, Michael D. Zhejiang Univ Coll Civil Engn & Architecture 866 Yuhangtang Rd Hangzhou 310058 Peoples R China Univ Calif San Diego Dept Struct Engn La Jolla CA USA

Civil structures may deteriorate during their service life due to degradation or damage imposed by natural hazards such as earthquakes, wind, and impact. Structural performance anomaly detection is essential to provide an early warning of structural degradation limit states in order to prevent potential catastrophic failure. Data-driven machine learning approaches have been widely used for this, due to their capability in capturing features sensitive to damage-induced anomalies from structural health monitoring (SHM) data, assuming that such data are available. Although machine learning models have been used, many are challenged by the vast operational and environmental variability that can corrupt SHM data and by (typically) strongly correlated information from different sensors in the SHM data. This paper proposes an unsupervised deep learning approach for the detection of structural anomaly based on a deep convolutional variational autoencoder (DCVAE) for feature extraction coupled with support vector data description (SVDD) for anomaly detection. The proposed DCVAE-SVDD method has several appealing strengths. First, the variational latent encoding is used to capture the features of monitoring data through a probability distribution. The integration of the Kullback-Leibler divergence in the loss function provides accurate estimation of the probability distributions. Second, the DCVAE designed with convolutional and deconvolutional operations utilizes the correlation among multisensor data to avoid loss of correlation features and achieve better performance in feature extraction. Third, the SVDD is utilized to create a minimum-volume hypersphere that contains the anomaly-sensitive statistical features of the state. The hypersphere accurately separates anomaly-sensitive statistical features of reference states of structure from the anomalous ones. A computational frame model and a laboratory grandstand model are used to evaluate the performance of the proposed meth

关键词： Structural anomaly detection structural health monitoring variational autoencoder support vector data description unsupervised deep learning

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Physically reliable 3D styled shape generation via structure-aware topology optimization in unified latent space

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COMPUTER-AIDED DESIGN 2025年 183卷

作者： Ijaz, Haroon Wang, Xuwei Chen, Wei Lin, Hai Li, Ming Zhejiang Univ State Key Lab CAD&CG Hangzhou 310027 Peoples R China

We propose a novel approach to structure-aware topology optimization (SATO) to generate physically plausible multi-component structures with diverse stylistic variations. Traditional TO methods often operate within a discrete voxel-defined design space, overlooking the underlying structure-aware, which limits their ability to accommodate stylistic design preferences. Our approach leverages variational autoencoders (VAEs) to encode both geometries and corresponding structures into a unified latent space, capturing part arrangement features. The design target is carefully formulated as a topology optimization problem taking the VAE code as design variables under physical constraints, and solved numerically via analyzing the associated sensitivity with respect to the VAE variables. Our numerical examples demonstrate the ability to generate lightweight structures that balance geometric plausibility and structural performance with much enhanced stiffness that outperforms existing generative techniques. The method also enables the generation of diverse and reliable designs, maintaining structural integrity throughout, via a direct smooth interpolation between the optimized designs. The findings highlight the potential of our approach to bridge the gap between generative design and physics-based optimization by incorporating deep learning techniques.

关键词： Generative design 3D shape creation Topology optimization variational autoencoder Finite element analysis Global convergent MMA

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Low frequency residential load monitoring via feature fusion and deep learning

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 238卷

作者： Ji, Tianyao Chen, Jiawei Zhang, Luliang Lai, Hongfeng Wang, Jian Wu, Qinghua South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Peoples R China

Non-intrusive load monitoring (NILM) is a technique used to disaggregate the total power signal into individual appliance power signals, which plays an important role in smart grid. Recently, deep learning is widely used to deal with the NILM problem. However, current deep learning models are purely data-driven, which do not consider physical mechanisms, making them less effective in extracting useful features. To address these issues, a new approach for feature extraction based on variational mode decomposition (VMD) and a new deep learning model based on variational autoencoder (VAE) are developed in this paper. The proposed feature extraction approach extracts the pulse feature and concatenates it with the original power data to form multiple features, i.e., which achieves feature fusion to improve the performance of deep learning models better than with a single feature. In addition, a feedback variational mode decomposition (FVMD) is proposed to improve the decomposition performance of the original VMD. The channel attention mechanism is introduced to VAE to improve the performance of the model. To verify the accuracy and robustness of the proposed scheme in NILM, it is compared with the state-of-the-art models on the UK-DALE dataset, and the results show that the proposed feature extraction approach can greatly improve the performance of deep learning models and the proposed new deep learning model outperforms some state-of-the-art models in the realm of NILM.

关键词： Non-intrusive load monitoring Feature fusion Feedback variational mode decomposition variational autoencoder Channel attention mechanism

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