Industrial Anomaly Detection

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Overview

Industrial anomaly detection aims to identify defects or unusual behavior when examples of failures are scarce and normal operation can vary across products, machines, and conditions. Visual methods often learn normal patterns with reconstruction models, patch-memory methods such as PatchCore, or pretrained vision features; synthetic defects and vision-language models are increasingly used to extend detection to new defect types. For sensor and audio data, common approaches include forecasting and reconstruction, autoencoders, and graph models that capture relationships among signals, with benchmarks such as MVTec AD, VisA, SWaT, and WADI. A central challenge is moving from strong results on curated benchmarks to reliable deployment under shifts, contaminated training data, tight false-alarm limits, and edge-device constraints.

Latest papers

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260 papers33 papers in the last four weeks, up 38% on the four weeks before. 0.2% of all new papers.

  1. Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detection

    Apr 6, 2026Yihan Sun, Yuqi Cheng, Junjie Zu +5Point Cloud Anomaly DetectionIndustrial Anomaly Detection

    the National Center of Technology Innovation for Intelligent Design and Numerical Control, Huazhong University of Science and Technology, Wuhan 430074, China · the Department of Intelligent Manufacturing, Contemporary Amperex Technology Ltd., Ningde 352000, China · the Institute for Infocomm Research, A*STAR, Singapore 138632 +2

  2. Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detection

    Apr 5, 2026Xueyang Kang, Zizhao Li, Tian Lan +3Point Cloud Anomaly DetectionAnomaly Detection

    The University of Melbourne, Parkville, Victoria 3010, Australia · Tsinghua University, Haidian District, Beijing 100871, China · The University of New South Wales, Sydney, NSW 2052, Australia +1

  3. UniSpector: Towards Universal Open-set Defect Recognition via Spectral-Contrastive Visual Prompting

    Apr 3, 2026Geonuk Kim, Minhoi Kim, Kangil Lee +5Industrial Anomaly DetectionIndustrial Inspection

    LG Energy Solution

  4. Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defects

    Apr 1, 2026Hanzhe Liang, Luocheng Zhang, Junyang Xia +7Point Cloud Anomaly DetectionIndustrial Anomaly Detection

    Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Shenzhen Audencia Financial Technology Institute, Shenzhen, China · School of Computer Science, Hangzhou Dianzi University, Hangzhou, China +4

  5. CausalPulse: An Industrial-Grade Neurosymbolic Multi-Agent Copilot for Causal Diagnostics in Smart Manufacturing

    Mar 31, 2026Chathurangi Shyalika, Utkarshani Jaimini, Cory Henson +1Smart ManufacturingRoot Cause Analysis

    Artificial Intelligence Institute, University of South Carolina · Stride Lab, University of Michigan, Dearborn · Bosch Center for Artificial Intelligence, Pittsburgh

  6. EngineAD: A Real-World Vehicle Engine Anomaly Detection Dataset

    Mar 26, 2026Hadi Hojjati, Christopher Roth, Rory Woods +2Anomaly DetectionTime-Series Anomaly Detection

    Department of Electrical and Computer Engineering, McGill University, CA · MILA - Quebec AI Institute, CA · Preteckt Inc., Hamilton, ON, Canada

  7. Template-Based Feature Aggregation Network for Industrial Anomaly Detection

    Mar 24, 2026Wei Luo, Haiming Yao, Wenyong YuIndustrial Anomaly DetectionReconstruction-Based Anomaly Detection

    State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, 100084, China · State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

  8. A Feature Shuffling and Restoration Strategy for Universal Unsupervised Anomaly Detection

    Mar 24, 2026Wei Luo, Haiming Yao, Zhenfeng Qiang +2Unsupervised Anomaly DetectionAnomaly Detection

    State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, 100084, China · School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China

  9. URA-Net: Uncertainty-Integrated Anomaly Perception and Restoration Attention Network for Unsupervised Anomaly Detection

    Mar 24, 2026Wei Luo, Peng Xing, Yunkang Cao +3Unsupervised Anomaly DetectionIndustrial Anomaly Detection

    State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China · School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China · State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

  10. Multimodal Industrial Anomaly Detection via Geometric Prior

    Mar 24, 2026Min Li, Jinghui He, Gang Li +3Industrial Anomaly DetectionAnomaly Localization

    Faculty of Data Science, City University of Macau, Macau, China. · Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China. · Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, China.

  11. Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection

    Mar 23, 2026Mingle Zhou, Jiahui Liu, Jin Wan +2Continual Anomaly DetectionUnsupervised Anomaly Detection

    Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China · Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, China · Faculty of Data Science, City University of Macau, Macau, China

  12. Distilled Large Language Model-Driven Dynamic Sparse Expert Activation Mechanism

    Mar 21, 2026Qinghui Chen, Zekai Zhang, Zaigui Zhang +5Industrial Anomaly DetectionIndustrial Inspection

    School of Control Science and Engineering, Shandong University, Jinan, China · Laoshan Laboratory, Qingdao, China · Jinan Inspur Data Technology Co., Ltd, Jinan, China +2

  13. Hybrid Autoencoder-Isolation Forest approach for time series anomaly detection in C70XP cyclotron operation data at ARRONAX

    Mar 20, 2026F Basbous, F Poirier, F Haddad +1Time-Series Anomaly DetectionIndustrial Anomaly Detection

    Nantes Univ, GIP ARRONAX · ARRONAX, Saint Herblain, France · also at Nantes Université, Nantes, France +5

  14. Balancing Performance and Fairness in Explainable AI for Anomaly Detection in Distributed Power Plants Monitoring

    Mar 19, 2026Corneille Niyonkuru, Marcellin Atemkeng, Gabin Maxime Nguegnang +1Semi-Supervised Anomaly DetectionAnomaly Detection

    African Institute for Mathematical Sciences (AIMS), Kigali, Rwanda. · Department of Mathematics, Rhodes University, PO Box 94, Makhanda, 6140, South Africa. · National Institute for Theoretical and Computational Sciences (NITheCS), Stellenbosch, 7600, South Africa. +2

  15. Cyber-Resilient Digital Twins: Discriminating Attacks for Safe Critical Infrastructure Control

    Mar 19, 2026Mohammadhossein Homaei, Iman Khazrak, Rubén Molano +2CPS SecurityIndustrial Control System Security

    Departamento de Ingeniería de Sistemas Informáticos y Telemáticos, Universidad de Extremadura, Av. Universidad S/N, Escuela Politécnica, Cáceres, 10003, Extremadura, Spain · Department of Computer Science, Bowling Green State University, Bowling Green, 43403, OH, USA

  16. Efficient Visual Anomaly Detection at the Edge: Enabling Real-Time Industrial Inspection on Resource-Constrained Devices

    Mar 18, 2026Arianna Stropeni, Fabrizio Genilotti, Francesco Borsatti +3Industrial Anomaly DetectionEdge Inference

    University of Padua, Padua, Italy

  17. One-to-More: High-Fidelity Training-Free Anomaly Generation with Attention Control

    Mar 18, 2026Haoxiang Rao, Zhao Wang, Chenyang Si +4Synthetic Anomaly GenerationIndustrial Anomaly Detection

    Nanjing University · China Mobile Zijin Innovation Institute · Shandong University of Science and Technology

  18. TinyGLASS: Real-Time Self-Supervised In-Sensor Anomaly Detection

    Mar 17, 2026Pietro Bonazzi, Rafael Sutter, Luigi Capogrosso +2Anomaly DetectionIndustrial Anomaly Detection

    ETH Zurich, Zurich, Switzerland · Interdisciplinary Transformation University of Austria, Linz, Austria · Swiss Engineering Partners AG, Zurich, Switzerland

  19. GATE-AD: Graph Attention Network Encoding For Few-Shot Industrial Visual Anomaly Detection

    Mar 16, 2026Aggelos Psiris, Yannis Panagakis, Maria Vakalopoulou +1Few-Shot Anomaly DetectionIndustrial Anomaly Detection

    Harokopio University of Athens, Athens, Greece · National and Kapodistrian University of Athens, Athens, Greece · Archimedes, Athena Research Center, Athens, Greece +1

  20. Graph-Based Deep Learning for Intelligent Detection of Energy Losses, Theft, and Operational Inefficiencies in Oil & Gas Production Networks

    Mar 15, 2026AbdulQoyum A. Olowookere, Adewale U. Oguntola, Ebenezer. Leke OdekanleIndustrial Anomaly DetectionAnomaly Detection

    Department of Chemical and Petroleum Engineering, Abiola Ajimobi Technical University, Ibadan, Oyo State, Nigeria. · Department of Computer Science, Abiola Ajimobi Technical University, Ibadan, Oyo State, Nigeria

  21. Multi-Period Texture Contrast Enhancement for Low-Contrast Wafer Defect Detection and Segmentation

    Mar 15, 2026Zihan ZhangImage SegmentationIndustrial Anomaly Detection

    Engineering Technology Research Center of Henan Province for MEMS Manufacturing and Applications, School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou 450001, China

  22. VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction

    Mar 14, 2026Hiroto Nakata, Yawen Zou, Shunsuke Sakai +5Anomaly DetectionIndustrial Anomaly Detection

    Department of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui-city, 910-8507, Japan · Graduate School of Science and Engineering, University of Toyama, 3190 Gofuku, Toyama-city, 930-8555, Japan · Faculty of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui-city, 910-8507, Japan +1

  23. AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison

    Mar 14, 2026Xi Jiang, Yue Guo, Jian Li +7Industrial Anomaly DetectionIndustrial Inspection

    Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China · Macau University of Science and Technology, Macau, China · Tencent YouTu Lab, Shenzhen and Shanghai, China +3

  24. MIRAGE: Model-agnostic Industrial Realistic Anomaly Generation and Evaluation for Visual Anomaly Detection

    Mar 13, 2026Jinwei Hu, Francesco Borsatti, Arianna Stropeni +3Synthetic Anomaly GenerationIndustrial Anomaly Detection

    University of Padova, Italy

  25. Spatio-Temporal Attention Graph Neural Network: Explaining Causalities With Attention

    Mar 11, 2026Kosti Koistinen, Kirsi Hellsten, Joni Herttuainen +1Industrial Anomaly DetectionIndustrial Control System Security

    Aalto University School of Science Computer Science Department P.O.Box 11000, 00076 AALTO, Finland

  26. Synthetic Defect Image Generation for Power Line Insulator Inspection Using Multimodal Large Language Models

    Mar 9, 2026Xuesong Wang, Caisheng WangSynthetic Data AugmentationIndustrial Inspection

    Department of Electrical and Computer Engineering, Wayne State University, 42 W. Warren Ave., Detroit, 48201, MI, USA

  27. Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams

    Mar 9, 2026Natalia Wojak-Strzelecka, Szymon Bobek, Grzegorz J. Nalepa +1Industrial Anomaly DetectionFault Detection

    1Jagiellonian Human-Centered AI Lab, Mark Kac Center for Complex Systems Research, Institute of Applied Computer Science, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, ul. prof. Stanisława Łojasiewicza 11, 30-348 Krakow, Poland · Institute of Computing Science, Poznań University of Technology, 60-965 Poznań, Poland

  28. Integration of deep generative Anomaly Detection algorithm in high-speed industrial line

    Mar 8, 2026Niccolò Ferrari, Nicola Zanarini, Michele Fraccaroli +2Industrial Anomaly DetectionOnline Anomaly Detection

    Department of Engineering, University of Ferrara, Via Saragat 1, Ferrara, 44122, Italy. · Bonfiglioli Engineering, Via Amerigo Vespucci 20, Ferrara, 44124, Italy.

  29. GRD-Net: Generative-Reconstructive-Discriminative Anomaly Detection with Region of Interest Attention Module

    Mar 8, 2026Niccolò Ferrari, Michele Fraccaroli, Evelina LammaIndustrial Anomaly DetectionAnomaly Localization

    Department of Engineering, University of Ferrara, Via Saragat 1, 44122 Ferrara, Italy · Bonfiglioli Engineering, Via Amerigo Vespucci 20, 44124 Ferrara, Italy

  30. Toward Multimodal Industrial Fault Analysis: A Single-Speed Chain Conveyor Dataset with Audio and Vibration Signals

    Mar 7, 2026Zhang Chen, Yucong Zhang, Xiaoxiao Miao +1Industrial Fault DiagnosisFault Detection

    Digital Innovation Research Center, Duke Kunshan University, Kunshan, China · School of Computer Science, Wuhan University, Wuhan, China · School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen, China +1