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

  1. FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Models

    May 9, 2026Jonas Petersen, Pratik Jawahar, Riccardo Maggioni +11ManufacturingIndustrial Anomaly Detection

    Forgis · ETH Zurich · Cairo University +3

  2. Real-IAD MVN: A Multi-View Normal Vector Dataset and Benchmark for High-Fidelity Industrial Anomaly Detection

    May 8, 2026Wenbing Zhu, Jianing Liang, Linjie Cheng +7Industrial Anomaly DetectionMultimodal Anomaly Detection

    1Fudan University · 4Rongcheer Co., Ltd. · 3Donghua University +1

  3. Two Steps Are All You Need: Efficient 3D Point Cloud Anomaly Detection with Consistency Models

    May 6, 2026Pranav A, Shashank B, Pranav Siddappa +3Point Cloud Anomaly DetectionIndustrial Anomaly Detection

    1R.V. College of Engineering · 2Technical University of Applied Sciences Würzburg-Schweinfurt

  4. LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturing

    May 5, 2026Ahmadreza Eslaminia, Chuhan Cai, Cameron Smith +5Multi-Agent LLM SystemsIndustrial Anomaly Detection

    Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA · Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA · Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA +1

  5. PhaseNet++: Phase-Aware Frequency-Domain Anomaly Detection for Industrial Control Systems via Phase Coherence Graphs

    Apr 30, 2026Raviteja Bommireddy, Varshith Bandaru, Lohith Pakala +1Industrial Anomaly DetectionAnomaly Detection

    Department of CSE IIITDM Kancheepuram Chennai, India

  6. WaferSAGE: Large Language Model-Powered Wafer Defect Analysis via Synthetic Data Generation and Rubric-Guided Reinforcement Learning

    Apr 30, 2026Ke Xu, Zhongyuan LianVLM EvaluationVisual Question Answering

    Shanghai Huahong Grace Semiconductor Manufacturing Corporation, Shanghai, 201203, China · Dept. of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

  7. SynSur: An end-to-end generative pipeline for synthetic industrial surface defect generation and detection

    Apr 29, 2026Paul Julius Kühn, Mika Pommeranz, Arjan Kuijper +1Synthetic Data AugmentationIndustrial Anomaly Detection

    Fraunhofer IGD, 64283 Darmstadt, Germany

  8. Hard to See, Hard to Label: Generative and Symbolic Acquisition for Subtle Visual Phenomena

    Apr 24, 2026Renjith Prasad, Rishabh Sharma, Andrew E. Shao +8Industrial Anomaly DetectionActive Learning

    University of South Carolina · AI Research Lab, HPE Labs, Hewlett Packard Enterprise · Indian AI Research Organization +1

  9. Text-Guided Multimodal Unified Industrial Anomaly Detection

    Apr 24, 2026Zewen Li, Shuo Ye, Zitong Yu +2Unified Multimodal ModelsIndustrial Anomaly Detection

    School of Computer Science & Software Engineering, Shenzhen University, China, 518060 · School of Computing and Information Technology, Great Bay University, Dongguan, 523000,China

  10. Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring

    Apr 22, 2026Natalia Martinez Gil, Fearghal O'Donncha, Wesley M. Gifford +3Interpretable Anomaly DetectionIndustrial Anomaly Detection

    IBM Research

  11. PLaMo 2.1-VL Technical Report

    Apr 21, 2026Tommi Kerola, Yuya Masuda, Takashi Masuko +5Visual Question AnsweringVision-Language Models

    Preferred Networks, Inc. (PFN)

  12. Feature Perturbation Pool-based Fusion Network for Unified Multi-Class Industrial Defect Detection

    Apr 21, 2026Yuanchan Xu, Wenjun Zang, Ying WuIndustrial Anomaly Detection

    School of Information Engineering, Sichuan University, China

  13. Industrial Surface Defect Detection via Diffusion Generation and Asymmetric Student-Teacher Network

    Apr 21, 2026Shuo Feng, Runlin Zhou, Yuyang Li +1Industrial Anomaly DetectionSynthetic Anomaly Generation

    School of Automation, Southeast University, Nanjing, China

  14. When Can We Trust Deep Neural Networks? Towards Reliable Industrial Deployment with an Interpretability Guide

    Apr 21, 2026Hang-Cheng Dong, Yuhao Jiang, Yibo Jiao +5Explainable Artificial IntelligenceIndustrial Anomaly Detection

  15. One-Step Diffusion with Inverse Residual Fields for Unsupervised Industrial Anomaly Detection

    Apr 20, 2026Boan Zhang, Wen Li, Guanhua Yu +3Industrial Anomaly DetectionUnsupervised Anomaly Detection

    Department of Computer Science and Technology Xidian University Shaanxi, China 710126 · Department of Electronic Engineering Xidian University Shaanxi, China 710126

  16. ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection

    Apr 20, 2026Qiuhui Chen, Jiaxiang Song, Shuai Tan +1Multimodal GroundingInterpretable Anomaly Detection

    School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

  17. H2VLR: Heterogeneous Hypergraph Vision-Language Reasoning for Few-Shot Anomaly Detection

    Apr 16, 2026Jianghong Huang, Luping Ji, Weiwei Duan +1Few-Shot Anomaly DetectionMedical Image Anomaly Detection

    School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

  18. Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study

    Apr 15, 2026Sergej Krasnikov, Lukas Meitz, Samineh Bagheri +3Time-Series Anomaly DetectionIndustrial Anomaly Detection

    Universität Augsburg Augsburg, Germany · Technische Hochschule Augsburg Augsburg, Germany · Independent Researcher Karlsruhe, Germany

  19. IAD-Unify: Task-Specific Interfaces for Industrial Anomaly Understanding, Segmentation, and Generation

    Apr 14, 2026Haoyu Zheng, Jiang Liu, Jiaqi Zhu +3Industrial Anomaly DetectionMultimodal Anomaly Detection

    Zhejiang University, China · National University of Singapore, Singapore · Cornell University, USA

  20. Neuromorphic Continual Learning for Sequential Deployment of Nuclear Plant Monitoring Systems

    Apr 13, 2026Samrendra Roy, Sajedul Talukder, Syed Bahauddin AlamIndustrial Control System SecurityIndustrial Anomaly Detection

    University of Illinois Urbana-Champaign Department of Nuclear, Plasma & Radiological Engineering Urbana, IL, USA · University of Texas at El Paso Department of Computer Science El Paso, TX, USA · National Center for Supercomputing Applications Urbana, IL, USA

  21. MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models

    Apr 13, 2026Xincheng Yao, Zefeng Qian, Chao Shi +2Anomaly DetectionIndustrial Anomaly Detection

    School of Information Science and Electronic Engineering, Shanghai Jiao Tong University · MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University

  22. Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection

    Apr 10, 2026Jennifer Werner, Justus Arweiler, Indra Jungjohann +4Synthetic Data GenerationIndustrial Anomaly Detection

    Fraunhofer Institute for Industrial Mathematics (ITWM), Kaiserslautern, Germany · Laboratory of Engineering Thermodynamics (LTD), RPTU Kaiserslautern, Germany

  23. CAD 100K: A Comprehensive Multi-Task Dataset for Car Related Visual Anomaly Detection

    Apr 10, 2026Jiahua Pang, Ying Li, Dongpu Cao +10Anomaly DetectionIndustrial Anomaly Detection

    Beijing Institute of Technology · Tsinghua University · China Agricultural University +3

  24. Large-Scale Universal Defect Generation: Foundation Models and Datasets

    Apr 10, 2026Yuanting Fan, Jun Liu, Bin-Bin Gao +5Synthetic Anomaly GenerationSynthetic Data Augmentation

    Tencent Youtu Lab, Shenzhen, China

  25. GroundingAnomaly: Spatially-Grounded Diffusion for Few-Shot Anomaly Synthesis

    Apr 9, 2026Yishen Liu, Hongcang Chen, Pengcheng Zhao +8Synthetic Anomaly GenerationDiffusion-Based Image Generation

    Beijing Institute of Technology · Beijing Jiaotong University · Li Auto +1

  26. AnomalyAgent: Agentic Industrial Anomaly Synthesis via Tool-Augmented Reinforcement Learning

    Apr 9, 2026Jiaming Su, Tengchao Yang, Ruikang Zhang +3Synthetic Anomaly GenerationTool-Using Agents

    Shanghai Jiao Tong University Shanghai, China · Tongji University Shanghai, China · Fudan University Shanghai, China

  27. Novel Anomaly Detection Scenarios and Evaluation Metrics to Address the Ambiguity in the Definition of Normal Samples

    Apr 8, 2026Reiji Saito, Satoshi Kamiya, Kazuhiro HottaAnomaly DetectionIndustrial Anomaly Detection

    Meijo University, 1-501 Shiogamaguchi, Tempaku-ku, Nagoya 468-8502, Japan

  28. SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection

    Apr 7, 2026Letian Bai, Chengyu Tao, Juan DuAnomaly DetectionMultimodal Anomaly Detection

    Smart Manufacturing Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 511453, China · College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China · The Hong Kong University of Science and Technology, Hong Kong SAR, 999077, China

  29. PASTA: Vision Transformer Patch Aggregation for Weakly Supervised Target and Anomaly Segmentation

    Apr 7, 2026Melanie Neubauer, Elmar Rueckert, Christian RauchWeakly Supervised Image SegmentationIndustrial Anomaly Detection

    Chair of Cyber Physical Sytems, Technical University of Leoben, 8700 Leoben, Austria

  30. Cross-Machine Anomaly Detection Leveraging Pre-trained Time-series Model

    Apr 7, 2026Yangmeng Li, Kei Sano, Toshihiro Kitao +4Anomaly DetectionIndustrial Anomaly Detection

    Program of Operations Research and Industrial Engineering, The University of Texas at Austin, 204 E. Dean Keeton Street, Austin, TX 78712-1139, USA. · Equipment Intelligence & App R&D Department, Tokyo Electron Ltd., Daido Seimei Sapporo Building, 1-3 Kita 3-jo Nishi, Chuo-ku, Sapporo, Hokkaido 060-0003, Japan. · Walker Department of Mechanical Engineering, The University of Texas at Austin, 204 E. Dean Keeton Street, Austin, TX 78712-1139, USA.