Industrial Fault Diagnosis

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27 papers4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

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
S2S-FDD: Bridging Industrial Time Series and Natural Language for Explainable Zero-shot Fault Diagnosis
Mar 9, 2026Baoxue Li, Chunhui ZhaoFault Diagnosis Industrial Fault Diagnosis
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China · School of Information and Electrical Engineering, Zhejiang University City College, Hangzhou, China
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 Diagnosis Fault 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
Bi-directional digital twin prototype anchoring with multi-periodicity learning for few-shot fault diagnosis
Mar 7, 2026Pengcheng Xia, Zhichao Dong, Yixiang Huang +3Fault Diagnosis Industrial Fault Diagnosis
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, PR China
CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing
Mar 6, 2026Mohammadhossein Ghahramani, Mengchu ZhouIndustrial Fault Diagnosis Industrial Anomaly Detection
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
Explainability for Fault Detection System in Chemical Processes
Feb 18, 2026Georgios Gravanis, Dimitrios Kyriakou, Spyros Voutetakis +2Industrial Fault Diagnosis Fault Diagnosis
Department of Information and Electronic Engineering, International Hellenic University, Greece · Chemical Process and Energy Resources Institute, Centre for Research and Technology Hellas, Greece · Department of Industrial Engineering and Management, International Hellenic University, Greece

Oct 19, 2026J/K move · Enter open · S save

Motor compound fault diagnosis using an attention-based cross-talk architecture

Wonjun Yi, Wonho Jung, Junho Kim, Rismaya Kumar Mishra+1

Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

Fault Diagnosis Industrial Fault Diagnosis Multi-Task Learning Bearing Fault Diagnosis

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As physical AI increasingly relies on sensing to understand physical systems, reliable condition monitoring of industrial machinery has become more important. Motors are ubiquitous in industrial systems, and increasing system complexity raises the likelihood of simultaneous faults. Accordingly, compound fault diagnosis that identifies multiple fault components and their severities is increasingly required. Because different fault components jointly affect the same vibration signal, their severity classification can be formulated as related tasks, making multi-task learning (MTL) suitable for exploiting shared information. However, conventional shared-trunk architectures force tasks to share a common representation, potentially causing negative transfer because fault components are characterized by different time-frequency regions. To address this issue, this study proposes a Residual Neural Dimension Reductor with Time-Frequency Channel Attention (RNDR-TFCA), an attention-enhanced cross-talk architecture for multi-output compound fault diagnosis. RNDR-TFCA preserves task-specific feature extractors while selectively exchanging information across tasks. Time-frequency attention emphasizes informative intra-task regions, while channel attention enhances useful inter-task information after nonlinear feature fusion. Experiments on a rotor-bearing system with inner-race fault, outer-race fault, misalignment, and unbalance showed that RNDR-TFCA achieved a macro F1 score of 0.898, outperforming single-task, multi-class, shared-trunk, and cross-talk baselines. Ablation experiments confirmed the benefit of jointly applying both attention mechanisms. RNDR-TFCA required 713 K parameters and 170.42 MMACs, only slightly higher than RNDR. Channel-expanded baselines still underperformed RNDR-TFCA, indicating that the gain was not simply attributable to increased model capacity.