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

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

  1. ConceptADapt: Concept-guided Adaptive Feature Reconstruction with Dynamic Attention for Few-Shot Industrial Anomaly Detection

    Aug 6, 2026Yufei Li, Yicheng Ruan, Long Tian +2Few-Shot Anomaly DetectionIndustrial Anomaly Detection

    School of Computer Science and Technology, Xidian University, Xi’an, China

  2. Grounded Well-Condition Anomaly Detection on the Volve Field: Constructed Labels, a Baseline, and a Dual-Head Model

    Aug 6, 2026Gospel Bassey, Samuel Bassey, Vincent FakiyesiIndustrial Anomaly DetectionFault Detection

    Independent Researcher. · College of Engineering, Biochemical Engineering. University of Georgia. · College of Engineering, EETI. University of Georgia.

  3. MGSB: Manifold Gated Signature Branch Pressure-Domain Baseline Architecture for Two-Phase Pipeline Flows Under Distributional Shift

    Aug 5, 2026Issah Suleiman, Sormeh Serpoosh, Nadine Elkholy +3Distribution Shift RobustnessOOD Generalization

  4. Keep the Needle, Prune the Haystack: Defect-Preserving Token Pruning for Efficient Zero-Shot Anomaly Detection

    Aug 4, 2026Yanning Hou, Jingyuan Zhang, Xiaoyun Wang +3Medical Image Anomaly DetectionIndustrial Anomaly Detection

    2National University of Defense Technology, Changsha, China · 1Anhui University, Hefei, China

  5. An Explainable LLM Agent Layer for Open-World Anomaly Detection in Oil Wells

    Aug 4, 2026Lucas Gouveia Omena Lopes, Thales Miranda de Almeida Vieira, Eduardo Toledo de Lima Junior +1Interpretable Anomaly DetectionLLM Agent Evaluation

    Laboratory of Scientific Computing and Visualization, Federal University of Alagoas, Maceio, Alagoas, Brazil · Computing Institute, Federal University of Alagoas, Maceio, Alagoas, Brazil

  6. FreqAnchorAD: Language-Free Zero-Shot Anomaly Detection via Frequency-Deviation Anchoring

    Aug 1, 2026Jianfeng Qiu, Peiyuan Li, Juan Xie +4Frequency-Domain Feature LearningMedical Image Anomaly Detection

    Anhui University, Hefei, China · National University of Defense Technology, Changsha, China

  7. OSAGEN: Object-Aware Mask Priors and Multistage Decoupled Diffusion for Industrial Anomaly Generation

    Jul 31, 2026Jinyi Xu, Peng Chen, Yunkang Cao +3Industrial Anomaly DetectionAnomaly Localization

    School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University · School of Artificial Intelligence and Robotics, Hunan University · Department of Computer and Information Science, University of Macau +1

  8. VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection

    Jul 31, 2026Peng Chen, Kaige Li, Wei Wang +5VLM AdaptationFrequency-Domain Feature Learning

    School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China

  9. ScratchSim: A Procedural Synthetic Data Pipeline for Surface Scratch Detection

    Jul 29, 2026Paul Julius Kühn, Saptarshi Neil Sinha, Tiago Kleist +3Synthetic-to-Real Domain AdaptationSynthetic Data Generation

    Fraunhofer IGD, Darmstadt, Germany · Fraunhofer IGD, Darmstadt Germany · Fraunhofer IGD & TU Darmstadt, Darmstadt, Germany +1

  10. Early Failure Prediction from Near-Anomaly Detection: A Proactive Approach

    Jul 29, 2026L{é}a Billet, Louise Trav{é}-Massuy{è}s, Elodie Chanthery +1Industrial Anomaly DetectionUnsupervised Anomaly Detection

    LAAS, INSA Toulouse · LAAS-CNRS, University of Toulouse, CNRS, Toulouse, France · LAAS-DISCO, Comue de Toulouse +1

  11. XMatchAD: A Cross-Modal Matching Perspective on Reconstruction-based Anomaly Detection

    Jul 26, 2026Mingxiu Cai, Zhe Zhang, Gaochang Wu +1Industrial Anomaly DetectionAnomaly Localization

    State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China

  12. Domain-Prior-Regularized Graph Modeling for Anomaly Detection in Cyber-Physical Systems

    Jul 25, 2026Youngseok Hwang, Joonsung Kwon, Geonwoo Lee +1Cyber-Physical SystemsGraph Representation Learning

    Graduate School of Data Science, Seoul National University, Seoul, South Korea.

  13. Label-free Industrial Fault Detection via Adversarial Inverse Reinforcement Learning: A System for Run-to-Failure Prognostics

    Jul 25, 2026Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley +1Reinforcement LearningIndustrial Anomaly Detection

    Deakin University · Waurn Ponds, Victoria, Australia

  14. Optimal Transport Image Representation and Deep Covariance Alignment (CORAL) for Control Valve Stiction Detection

    Jul 24, 2026Seshu K. DamarlaIndustrial Anomaly DetectionFeedback Control

    Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta, Canada

  15. Agentic Root Cause Analysis through Evidence-Grounded Reasoning

    Jul 24, 2026Amaury Wei, Olga FinkIndustrial Anomaly DetectionAgentic Reasoning

    EPFL - IMOS Laboratory CH-1015 Lausanne Switzerland

  16. MemNMF: Memory-Augmented NMF on LPC Spectra for Anomalous Sound Detection

    Jul 24, 2026Phurich Saengthong, Takahiro ShinozakiMemory-Augmented Neural NetworksNon-Negative Matrix Factorization

    Institute of Science Tokyo, Japan

  17. IMMoE: Incomplete Multi-View Anomaly Detection via Mixture of View Experts Fusion

    Jul 21, 2026Lei HuMixture of ExpertsIndustrial Anomaly Detection

    South China University of Technology, Guangzhou, China

  18. MOPDA: Mixed-Trajectory On-Policy Distillation for Language-Guided Industrial Anomaly Detection

    Jul 21, 2026Shuimu Chen, Jing Jin, Nan Su +5Industrial Anomaly DetectionVLM Distillation

    Tsinghua University · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) · Shenzhen University

  19. O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning

    Jul 20, 2026Mei Yuan, Qi Long, Qifeng Wu +5Industrial Anomaly DetectionAnomaly Localization

    Carnegie Mellon University · University of Alabama at Birmingham · Griffith University

  20. Knowledge-Assisted Multi-Graph Dependency Learning for Multivariate Time Series Anomaly Detection in Multi-Stage Industrial Processes

    Jul 17, 2026Jaeyeong Lee, Taeseong Yoon, Wonmo Koo +1Graph Structure LearningGraph Representation Learning

    Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

  21. M2P-AD: Memory-to-Prototype Learning with Boundary-aware Score Refinement for 3D Anomaly Detection

    Jul 15, 2026Seyoung Jeong, Jong Pil Yun, Sang Jun LeePrototype LearningIndustrial Anomaly Detection

    Jeonbuk National University, Jeonju, Republic of Korea · Manufacturing AI Research Center, Korea Institute of Industrial Technology (KITECH), Incheon, Republic of Korea

  22. A Masked Autoencoder Approach to Unsupervised Steel Surface Defect Recognition

    Jul 14, 2026Shrey PatelMasked AutoencodersIndustrial Anomaly Detection

    University of Maryland, College Park · United States of America

  23. Statistical Non-linear Reconstruction Loss for Image Anomaly Detection

    Jul 14, 2026Nguyen Minh Tri, Hoang Khuong Duy, Huynh Cong Viet NguIndustrial Anomaly DetectionAnomaly Localization

    AIC-Lab, FPT University

  24. Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection

    Jul 14, 2026Martin Uray, Saverio Messineo, Roland Kwitt +1Industrial Anomaly DetectionZero-Shot Anomaly Detection

    Josef Ressel Centre for Intelligent and Secure Industrial Automation, Salzburg University of Applied Sciences, Austria · Department of Artificial Intelligence and Human Interfaces, Paris Lodron University of Salzburg, Austria

  25. Rough Path Signature-Guided Geometry Augmentation for Few-Shot Industrial Surface Defect Detection

    Jul 14, 2026Jiaqi KuangFew-Shot Anomaly DetectionData Augmentation

    Oxford Suzhou Centre for Advanced Research, University of Oxford Suzhou, China

  26. TC-MAF: Train-Calibrated Bounded Multi-Evidence Fusion for Multimodal Industrial Anomaly Detection

    Jul 13, 2026Ming Deng, Sijin Sun, Xiaochuan Hu +1Industrial Anomaly DetectionAnomaly Localization

    Shanghai University Shanghai, China · National University of Singapore Singapore, Singapore · University of Electronic Science and Technology of China Chengdu, China

  27. Physics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detection

    Jul 12, 2026Jian Ning, Qin Zou, Linchun Wu +4Point Cloud Anomaly DetectionIndustrial Anomaly Detection

    School of Computer Science, Wuhan University, Wuhan 430072, Hubei, China · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518060, Guangdong, China · School of Information and Software Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China +1

  28. CAAD: Causality-Aware Multivariate Time Series Anomaly Detection via Multi-Scale Alignment and Structural Causal Consistency

    Jul 9, 2026Xin Wang, Yunshi Wen, Yanan He +4Industrial Anomaly DetectionTime-Series Anomaly Detection

    Stony Brook University · Stony Brook, New York, USA · Rensselaer Polytechnic Institute +4

  29. Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems

    Jul 7, 2026Alexander Apartsin, Yehudit ApersteinMultimodal Anomaly DetectionIndustrial Anomaly Detection

    Holon Institute of Technology (HIT), Holon, Israel · Afeka Academic College of Engineering, Tel Aviv, Israel

  30. ProCon: Projection-Consistency Memory for Training-Free Anomaly Detection

    Jul 6, 2026Joongwon Chae, Lihui Luo, Yang Liu +4Industrial Anomaly DetectionAnomaly Localization

    Tsinghua University Shenzhen International Graduate School, Shenzhen, China · Affiliated Fifth Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China · Chinese Medicine Guangdong Laboratory +1