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

CardsList

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

  1. CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing

    Mar 6, 2026Mohammadhossein Ghahramani, Mengchu ZhouSmart ManufacturingIndustrial Fault Diagnosis

    Birmingham City University, UK · the Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA

  2. Technical Report: Automated Optical Inspection of Surgical Instruments

    Mar 6, 2026Zunaira Shafqat, Atif Aftab Ahmed Jilani, Qurrat Ul AinFault DetectionIndustrial Inspection

    Authors are with National University of Computer and Emerging Sciences (FAST-NUCES), Islamabad, Pakistan

  3. Cross-Modal Mapping and Dual-Branch Reconstruction for 2D-3D Multimodal Industrial Anomaly Detection

    Mar 4, 2026Radia Daci, Vito Renò, Cosimo Patruno +4Industrial Anomaly DetectionMultimodal Anomaly Detection

    CNR-ISASI, Institute of Applied Sciences and Intelligent Systems / National Research Council of Italy, via Monteroni snc, Lecce, 73100, Italy · CNR-STIIMA, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Via G. Amendola 122 D/O, Bari, 70126, Italy · Institute d’Electronique de Microelectronique et de Nanotechnologie (IEMN) / Universite Polytechnique Hauts de France; Universite de Lille; CNRS, UMR 8520, City, 59313, France

  4. Spatial Autoregressive Modeling of DINOv3 Embeddings for Unsupervised Anomaly Detection

    Mar 3, 2026Ertunc Erdil, Nico Schulthess, Guney Tombak +1Medical Image Anomaly DetectionIndustrial Anomaly Detection

    Computer Vision Lab. ETH Zurich, Zurich, Switzerland

  5. Improving Anomaly Detection with Foundation-Model Synthesis and Wavelet-Domain Attention

    Mar 3, 2026Wensheng Wu, Zheming Lu, Ziqian Lu +5Industrial Anomaly DetectionSynthetic Anomaly Generation

    School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, 310027, China · School of Computer Science and Technology(), , Hangzhou, 310018, China · Ningbo Innovation Center, Zhejiang University, Ningbo, China +2

  6. SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling

    Feb 26, 2026Camile Lendering, Erkut Akdag, Egor BondarevFew-Shot Anomaly DetectionAnomaly Detection

    AIMS Group, Department of Electrical Engineering, Eindhoven University of Technology

  7. Learning Unknown Interdependencies for Decentralized Root Cause Analysis in Nonlinear Dynamical Systems

    Feb 25, 2026Ayush Mohanty, Paritosh Ramanan, Nagi GebraeelRoot Cause AnalysisHeterogeneous Federated Learning

    School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332 · School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK 74708

  8. EAGLE: Expert-Augmented Attention Guidance for Tuning-Free Industrial Anomaly Detection in Multimodal Large Language Models

    Feb 19, 2026Xiaomeng Peng, Xilang Huang, Seon Han ChoiAnomaly DetectionIndustrial Anomaly Detection

    Ewha Womans University

  9. StructCore: Structure-Aware Image-Level Scoring for Training-Free Unsupervised Anomaly Detection

    Feb 19, 2026Joongwon Chae, Lihui Luo, Yang Liu +8Unsupervised Anomaly DetectionAnomaly Detection

    Tsinghua University Shenzhen International Graduate School, Shenzhen, China · Ratel Soft · Affiliated Fifth Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China +1

  10. How Much Does Machine Identity Matter in Anomalous Sound Detection at Test Time?

    Feb 18, 2026Kevin Wilkinghoff, Keisuke Imoto, Zheng-Hua TanAnomaly DetectionIndustrial Anomaly Detection

    Aalborg University, Denmark · Pioneer Centre for Artificial Intelligence, Denmark · Kyoto University, Japan

  11. Self-Evolving Multi-Agent Network for Industrial IoT Predictive Maintenance

    Feb 17, 2026Rebin Saleh, Khanh Pham Dinh, Balázs Villányi +1Predictive MaintenanceIndustrial Anomaly Detection

    Department of Electronics Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3, H-1111 Budapest, Hungary · DataScienceWorld.Kan, Hanoi, Vietnam · Department of Computer Science, The University of Alabama at Birmingham, 1720 University Blvd, Birmingham, AL 35294 USA

  12. Benchmarking IoT Time-Series AD with Event-Level Augmentations

    Feb 17, 2026Dmitry Zhevnenko, Ilya Makarov, Aleksandr Kovalenko +6Time-Series Anomaly DetectionAnomaly Detection

    AXXX, Moscow, Russia · Research Center of the Artificial Intelligence Institute, Innopolis University, Innopolis, Russia · Trusted AI Center, RAS, Moscow, Russia +1

  13. Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning

    Feb 10, 2026Peng Chen, Wenqiang Wang, Yunkang Cao +7Industrial Anomaly DetectionMultimodal Anomaly Detection

    School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China · School of Artificial Intelligence and Robotics, Hunan University, Changsha, China · Department of Computer and Information Science, University of Macau, Macau, China

  14. Auditing Frozen-Encoder Anomaly Detection Across Mechanical Systems: Representation Provenance, Calibration, and Protocol Effects

    Jan 16, 2026Jose Sánchez AndreuMahalanobis DistanceIndustrial Anomaly Detection

    Independent Researcher, Murcia, Spain

  15. LPCAN: Lightweight Pyramid Cross-Attention Network for Rail Surface Defect Detection Using RGB-D Data

    Jan 14, 2026Jackie Alex, Guoqiang HuanIndustrial Anomaly DetectionIndustrial Inspection

    St. Petersburg College

  16. Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT

    Jan 5, 2026Mohammed Ayalew Belay, Adil Rasheed, Pierluigi Salvo RossiCommunication-Efficient Distributed TrainingIndustrial Anomaly Detection

    Department of Electronic Systems, Norwegian University of Science and Technology, 7034 Trondheim, Norway

  17. Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation

    Dec 10, 2025Nadeem Nazer, Hongkuan Zhou, Lavdim Halilaj +2Industrial Anomaly DetectionMultimodal Anomaly Detection

    Corporate Research, Robert Bosch GmbH, Renningen, Germany · Otto-von-Guericke-University Magdeburg, Magdeburg, Germany · Institute for Artificial Intelligence, University of Stuttgart, Stuttgart, Germany +2

  18. AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization

    Aug 6, 2025Jingyi Liao, Yongyi Su, Rong-Cheng Tu +6VLM AdaptationIndustrial Anomaly Detection

    1Nanyang Technological University · Institute for Infocomm Research (I2R), A*STAR · 3South China University of Technology

  19. ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects

    Mar 6, 2025Paul J. Krassnig, Dieter P. GruberIndustrial Anomaly DetectionIndustrial Inspection

    Polymer Competence Center Leoben GmbH, Leoben, Austria. · Chair of Materials Science and Testing of Polymers, Montanuniversitat Leoben, Leoben, Austria.

  20. Tuned Reverse Distillation: Enhancing Multimodal Industrial Anomaly Detection with Crossmodal Tuners

    Dec 12, 2024Xinyue Liu, Jianyuan Wang, Biao Leng +1Cross-Modal Knowledge DistillationIndustrial Anomaly Detection

    School of Computer Science and Engineering, Beihang University, Beijing 100191, China · Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, and the School of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China · School of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, China