Industrial Fault Diagnosis

Latest papers

36 papers8 papers in the last four weeks, up 167% on the four weeks before. 0.1% of all new papers.

Cross-Asset Knowledge Transfer for Early Fault Detection of Data-Scarce Assets Without Target Fault Data
Nov 4, 2026Zekai Wang, Yuxuan Li, Zhangyue ShiZero-Shot Domain Adaptation Fault Detection
Charles F. Dolan School of Business, Fairfield University, Fairfield, CT, United States · School of Economics and Management, Tiangong University, Tianjin, China · Independent Researcher, Chesterfield, MO, United States
OperAItor: An Agentic Framework for Fault-Tolerant Operation of Chemical Processes
Nov 3, 2026Javal Vyas, Mehmet Mercang{ö}zLanguage Model-Based Control Process-Based Agent Evaluation
Autonomous Industrial Systems Laboratory, Department of Chemical Engineering, Imperial College London, Imperial College Road, South Kensington, London, SW7 2AZ, United Kingdom
Reconstruction-Based Diagnosis through Gödel-Regularized Virtual Observability
Nov 3, 2026Oliver Niggemann, Jonas Ehrhardt, Alexander DiedrichIndustrial Fault Diagnosis Fault Localization
Chair of Computer Science in Mechanical Engineering, Helmut Schmidt University, Hamburg, Germany
LaplaceDiag: Fault Diagnosis through Changes in Learned Actuator Effects
Nov 2, 2026Oliver NiggemannFault Diagnosis Industrial Fault Diagnosis
Helmut Schmidt University, Hamburg, Germany
Class-aware Latent Attention Replay (CLAR): Class Incremental Hazard Diagnosis for Resilient AI in the Manufacturing Industrial Internet
Oct 29, 2026Avranshu Mahanty, Ran Jin, Yingyan ZengReplay-Based Continual Learning Class-Incremental Learning
Department of Mechanical, Materials, and Industrial Engineering, University of Cincinnati, Cincinnati, OH · Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA
Physical-Variable-Informed Deep Learning for Robust Diagnosis in Industrial Fluid Transport Systems
Oct 20, 2026Yu Sha, Junqi Tao, Dixin Zhou +4Fault Diagnosis Industrial Fault Diagnosis
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China. · Frankfurt Institute for Advanced Studies, Frankfurt am Main, 60438, Germany. · Key Laboratory of Quark & Lepton Physics and Institute of Particle Physics, Central China Normal University, Wuhan, 430079, China. +4
Physical Operating-Condition-Guided Vision-Language Matching for Cavitation Diagnosis in Industrial Pipeline Systems
Oct 20, 2026Yu Sha, Junqi Tao, Deyu Wang +4Fault Diagnosis Industrial Fault Diagnosis
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China · Frankfurt Institute for Advanced Studies, Frankfurt am Main, 60438, Germany · Institut für Theoretische Physik, Goethe Universität Frankfurt, Frankfurt am Main, 60314, Germany +3
What Moved the Alarm Score? Causal Attribution with Exact Computation and Scarce-Incident Estimation
Oct 20, 2026Masaaki Takada, Ryusei ShingakiCausal Attribution Shapley Value Attribution
Toshiba Corporation
Motor compound fault diagnosis using an attention-based cross-talk architecture
Oct 19, 2026Wonjun Yi, Wonho Jung, Junho Kim +2Fault Diagnosis Industrial Fault Diagnosis
Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
Can LLM Agents Efficiently Identify Appliance Faults? Insights From Diagnosing a Simulated Appliance
Oct 13, 2026Lucas H. McCabe, Sarah Lukens, H. Howie HuangLLM Agents Fault Diagnosis
LMI Consulting · The George Washington University
Plant Memory: Technical Language Processing and a Verified Maintenance Ontology for Agent-Accessible Failure Knowledge from SAP PM Work Orders
Oct 13, 2026Akash RajIndustrial Fault Diagnosis Document Information Extraction
M.Tech, School of Artificial Intelligence and Data Science, Indian Institute of Technology Jodhpur
Agentic4PDM: Host-Governed Evidence Admissibility for Industrial Fault Diagnosis
Oct 13, 2026Nam V. Nguyen, Hoang Van Truong, Hieu D. D. +3Industrial Fault Diagnosis Agentic Reasoning
FPT Corporation · Independent Researcher · Aalborg University
When LLMs Sit Above Diagnostic Tools: Unrealized Complementarity in Industrial Fault Diagnosis
Oct 4, 2026Donghwan KimLLM Reliability LLM Tool Use
Independent Researcher
Neutrosophic Ensemble Classification for Uncertainty-Aware Bearing Fault Detection: Evidence from Laboratory and Variable-Speed Industrial Benchmarks
Sep 18, 2026Maikel Leyva-Vazquez, Dayron Rumbaut Rangel, Lorenzo Cevallos-Torres +1Uncertainty Quantification Industrial Fault Diagnosis
Universidad de Guayaquil, Guayaquil, Ecuador · Universidad Bolivariana del Ecuador, Guayaquil, Ecuador · Universidad Bernardo O'Higgins, Santiago, Chile
Robust Prototypical Networks for Few-Shot Sensor Fault Diagnosis
Sep 10, 2026Mohammed Ayalew Belay, Amirshayan Haghipour, Pierluigi Salvo RossiPrototype-Based Classification Fault Detection
Simula UiB, Bergen, Norway · Dept. of Electronic Systems, NTNU, Trondheim, Norway · Dept. of Gas Technology, SINTEF Energy Research, Trondheim, Norway
SeaCausal-FL: Federated Fuzzy Causal Learning for Maritime IoT Fault Diagnosis and Counterfactual Reasoning
Sep 5, 2026Yuhang Qiu, Haihan Zhu, Koteeswaran Seerangan +6Fault Diagnosis Industrial Fault Diagnosis
School of Science, Jimei University, Xiamen 361021, China · School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China · Department of Computer Science and Engineering, R. M. K. Engineering College (Autonomous), Kavaraipettai - 601206, Tamil Nadu, India +4
Multi-Class Electrical and Mechanical Fault Classification Using Random Convolutional Kernels
Aug 19, 2026Mouhamadou Mansour Lo, Mouad Talbaoui, Gildas Morvan +3Fault Diagnosis Industrial Fault Diagnosis
UR 4025, LSEE University of Artois F-62400 B´ethune, France · UR 3926, LGI2A University of Artois F-62400 B´ethune, France
ConMem: Contribution-Aware Memory for Long-Horizon Manufacturing Inspection Logs
Jul 30, 2026Bingchen Liu, Yuanyuan Fang, Lei Liu +6Memory-Augmented Language Models Predictive Maintenance
Shandong University, School of Software Jinan, Shandong, China · Boston University, Metropolitan College Boston, MA, USA · North China Electric Power University, School of Control and Computer Engineering Beijing, Beijing, China +3
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 Learning Industrial Anomaly Detection
Deakin University · Waurn Ponds, Victoria, Australia
A Multi-level Information Integration Framework for Physically Verifiable Fault Diagnosis of Rotating Machinery
Jul 24, 2026Yuntong Chen, Jianyu Liu, Yingqi Li +5Predictive Maintenance Fault Detection
School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China · Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore · School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China
Industrial Tokenization for LLM-Based Health Intelligence: A Federated Architecture for Industrial Evidence Integration
Jul 24, 2026Deshui Li, Xiao-Ming Yuan, Zishun WangIndustrial Fault Diagnosis
Mingyang Smart Energy Co., Ltd. Zhongshan, China
Physics-Informed Graph Learning with Uncertainty Awareness for Open-Set Domain Generalization in Fault Diagnosis
Jul 5, 2026Jinfeng Zhu, Shiyu Long, Ye YuanUncertainty Quantification Domain Generalization
College of Computer and Information Science, Southwest University, Tiansheng Road, Chongqing, 400715, China
DiagFlowBench: Evaluating How Language Models Handle Off-Procedure Inputs in Grounded Diagnostic Dialogue
Jun 16, 2026Guillermo Gil de Avalle, Laura Maruster, Shaina Raza +1LLM Evaluation LLM Grounding
University of Groningen, PO Box 72, 9700 AB Groningen, The Netherlands · Vector Institute for Artificial Intelligence, MaRS Centre, Toronto, ON, Canada
Progressive Knowledge-Guided Large Language Model Framework for Bearing Fault Diagnosis
Jun 15, 2026Jinghan Wang, Gaoliang Peng, Yanjun Chen +3Multi-Scale Feature Fusion Physics-Informed ML
Harbin Institute of Technology, China · Eastern Institute of Technology, China
FactoryLLM: A Safe and Open-Source AI Playground for Evaluating LLMs in Smart Factories
Jun 12, 2026Yash Pulse, Yong-Bin Kang, Abhik Banerjee +2Manufacturing LLM Evaluation
Swinburne University of Technology, Melbourne, Australia
A Reliable Fault Diagnosis Method Based on Belief Rule Base Consider Robustness Analysis
Jun 9, 2026Mingyuan Liu, Dan Yin, Zongzong WuFault Diagnosis Industrial Fault Diagnosis
Central South University, Changsha, Hunan, P.R. China.
YOTOnet: Zero-Shot Cross-Domain Fault Diagnosis via Domain-Conditioned Mixture of Experts
May 6, 2026Zesen Wang, Zihao Wu, Yue Hu +2Cross-Domain Transfer Learning Bearing Fault Diagnosis
School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China
Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health
Apr 22, 2026Peter Collett, Alexander Johannes Stasik, Simone Casolo +1Simulation-Based Inference Efficient Inference
Cognite AS, Oslo, Norway · Department of Data Science, Norwegian University of Life Sciences, Ås, Norway · Department for Mathematics and Cybernetics, SINTEF Digital, Oslo, Norway
Multi-Level Temporal Graph Networks with Local-Global Fusion for Industrial Fault Diagnosis
Apr 20, 2026Bibek Aryal, Gift Modekwe, Qiugang LuTemporal GNNs Multi-Scale Feature Fusion
Department of Chemical Engineering, Texas Tech University, Lubbock, TX 79409, USA
Deep Hierarchical Knowledge Loss for Fault Intensity Diagnosis
Apr 8, 2026Yu Sha, Shuiping Gou, Bo Liu +8Fault Diagnosis Industrial Fault Diagnosis
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China · Frankfurt Institute for Advanced Studies, Frankfurt am Main 60438, Germany · School of Artificial Intelligence, Xidian University, Xi’an 710126, China +5

Nov 4, 2026J/K move · Enter open · S save

Cross-Asset Knowledge Transfer for Early Fault Detection of Data-Scarce Assets Without Target Fault Data

Zekai Wang, Yuxuan Li, Zhangyue Shi

Charles F. Dolan School of Business, Fairfield University, Fairfield, CT, United States · School of Economics and Management, Tiangong University, Tianjin, China · Independent Researcher, Chesterfield, MO, United States

Zero-Shot Domain Adaptation Fault Detection Industrial Fault Diagnosis Domain-Adversarial Learning Bearing Fault Diagnosis

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Reliability management of industrial fleets depends on early fault warnings, yet lifecycle data across similar assets are highly imbalanced. A few assets accumulate run-to-failure histories, while newly installed or rarely failing assets provide only normal-condition data. This paper asks how diagnostic knowledge can be transferred from a data-rich source asset to a data-scarce target asset when no fault data have been observed on the target. We formulate cross-asset early fault detection as a zero-shot domain adaptation problem, in which zero-shot refers to the absence of target fault samples, and propose a framework that requires only normal data from the target. The framework comprises (1) a statistical process control step that labels the source asset run-to-failure data with an exponentially weighted moving average chart, reducing manual annotation to verifying flagged segments, (2) an Adapted Domain-Adversarial Neural Network (ADANN) that combines domain-adversarial training and a Maximum Mean Discrepancy penalty with an Adapted Classification-Domain (ACD) loss, which anchors the classifier normal region to the target own normal data, and (3) an ahead-of-time labeling scheme with a prediction horizon that targets incipient faults. Under an exact-classification assumption, a theoretical analysis characterizes the domain-adversarial term for a normal-only target and the alignment state in which source fault features remain separated from target normal features. On a public run-to-failure bearing dataset, ADANN attains the highest mean F1-score among four transfer baselines, with a nominally significant precision gain over the strongest baseline, and its robustness is examined under synthetic noise.