Bearing Fault Diagnosis

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Overview

Bearing fault diagnosis studies how to detect and classify damage, such as inner-race or outer-race faults, from vibration and other sensor measurements before failures disrupt machinery. Common approaches transform signals into time-frequency maps or spectral features, then use convolutional networks, Transformers, state-space models, sparse coding, or physics-informed features to recognize fault patterns. Current work also tackles limited labels and changing operating conditions through self-supervised learning, domain adaptation, and cross-dataset pretraining, with benchmarks such as CWRU, Paderborn, and IMS used to test performance. A central challenge is whether high benchmark scores generalize to unseen physical bearings and industrial settings: leakage-free, bearing-wise evaluation often reveals transfer gaps, while robustness, uncertainty, and interpretability remain important goals.

Latest papers

CardsList

19 papers2 papers in the last four weeks, down 50% on the four weeks before. 0.0% of all new papers.

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

    Oct 19, 2026Wonjun Yi, Wonho Jung, Junho Kim +2Fault DiagnosisIndustrial Fault Diagnosis

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

  2. Agentic4PDM: Host-Governed Evidence Admissibility for Industrial Fault Diagnosis

    Oct 13, 2026Nam V. Nguyen, Hoang Van Truong, Hieu D. D. +3Industrial Fault DiagnosisAgentic Reasoning

    FPT Corporation · Independent Researcher · Aalborg University

  3. When Labels Are Scarce: An Oscillatory State Space Model for Vibration Diagnosis

    Sep 23, 2026Mainak Mallick, Seung-Kyum ChoiPredictive MaintenanceTiny ML

    Georgia Institute of Technology

  4. 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 QuantificationIndustrial Fault Diagnosis

    Universidad de Guayaquil, Guayaquil, Ecuador · Universidad Bolivariana del Ecuador, Guayaquil, Ecuador · Universidad Bernardo O'Higgins, Santiago, Chile

  5. FreqCondNorm: Towards Cross-domain Predictive Maintenance through a Frequency-Conditioned Transformer Foundation Model

    Sep 17, 2026Zaynab Raounak, Camille LHermine, Zhiguo ZengMulti-Source Domain AdaptationSelf-Supervised Time Series Representation Learning

    Laboratoire Génie Industriel, CentraleSupélec, Université Paris-Saclay, France

  6. When Does Domain Adaptation Help on Physical Vibration Sensors? A Held-Out-Bearing Study of Neural-Operator and Convolutional Models

    Sep 11, 2026Kumbha Nagaswetha, Rabi PathakUnsupervised Domain AdaptationBearing Fault Diagnosis

    India

  7. A Time-Frequency Dual-Domain Multi-Scale Convolutional Neural Network for Bearing Fault Diagnosis under Strong Noise

    Aug 10, 2026Yanxi Ding, Tingyue JiaMulti-Scale Feature FusionConvolutional Neural Networks

    School of Engineering China University of Petroleum-Beijing at Karamay Campus Karamay, China

  8. A Multi-level Information Integration Framework for Physically Verifiable Fault Diagnosis of Rotating Machinery

    Jul 24, 2026Yuntong Chen, Jianyu Liu, Yingqi Li +5Predictive MaintenanceFault 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

  9. Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods

    Jul 21, 2026Seyed Mohammadreza Alavi, Ardeshir Shojaeinasab, Reza Jalayer +2Synthetic Data GenerationTime Series Generation

    Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran · Department of Electrical and Computer Engineering, University of Victoria, Victoria BC, Canada · FAST-Lab, Faculty of Engineering and Natural Sciences, Tampere University, Korkeakoulunkatu 7, 33720 Tampere, Finland +1

  10. Parametric Generalized Adaptive Moment Features (PG-AMF) for Bearing Fault Diagnosis and Machine Health Monitoring

    Jun 24, 2026Rajeev KumarRepresentation LearningPredictive Maintenance

    Department of Mechanical Engineering University of Alberta Edmonton, Canada

  11. An LLM-based Two-Stage Transformer Framework for Cross-Domain Bearing Fault Diagnosis with Limited Data

    Jun 23, 2026Jinghan Wang, Feng Cheng, Wentao Wu +3Few-Shot Domain AdaptationPredictive Maintenance

    School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China

  12. Digital Twin-Driven Adaptive Sim-to-Real Alignment via Reinforcement Learning for Vibration-Based Bearing Health Monitoring Under Data Scarcity

    Jun 23, 2026Jinghan Wang, Yanjun Chen, Wei Zhang +3Synthetic-to-Real Domain AdaptationBearing Fault Diagnosis

    a Harbin Institute of Technology, China · b Eastern Institute of Technology, China

  13. Progressive Knowledge-Guided Large Language Model Framework for Bearing Fault Diagnosis

    Jun 15, 2026Jinghan Wang, Gaoliang Peng, Yanjun Chen +3Multi-Scale Feature FusionPhysics-Informed ML

    Harbin Institute of Technology, China · Eastern Institute of Technology, China

  14. TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detection

    Jun 2, 2026Xiancheng Wang, Zhibo Zhang, Ran Li +4Fault DetectionUnsupervised Anomaly Detection

    School of Ocean Engineering, Harbin Institute of Technology, West Wenhua Road, Weihai, 264209, Shandong, China · Technical Center, Bogie Development Department, CRRC Qingdao Sifang Locomotive and Rolling Stock Co., Ltd, Jinhong East Road, Qingdao, 266111, Shandong, China · Qingdao University, No. 308 Ningxia Road, Qingdao, 266071, Shandong, China

  15. YOTOnet: Zero-Shot Cross-Domain Fault Diagnosis via Domain-Conditioned Mixture of Experts

    May 6, 2026Zesen Wang, Zihao Wu, Yue Hu +2Cross-Domain Transfer LearningBearing Fault Diagnosis

    School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China

  16. Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis

    Apr 22, 2026Junyu Ren, Wensheng Gan, Philip S YuContrastive LearningCross-Domain Generalization

    College of Cyber Security, Jinan University, Guangzhou 510632, China · Department of Computer Science, University of Illinois Chicago, Chicago, USA

  17. LISTA-Transformer Model Based on Sparse Coding and Attention Mechanism and Its Application in Fault Diagnosis

    Mar 4, 2026Shuang Liu, Lina Zhao, Tian Wang +1Bearing Fault DiagnosisVision Transformer

    The College of Mathematics and Physics, Beijing University of Chemical Technology, China · The College of mechanical and electrical engineering, Beijing University of Chemical Technology, China

  18. Towards a more realistic evaluation of machine learning models for bearing fault diagnosis

    Sep 26, 2025João Paulo Vieira, Victor Afonso Bauler, Rodrigo Kobashikawa Rosa +1Bearing Fault Diagnosis

    Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis, Brazil

  19. CWT-Enhanced Vibration Sensing With Time-Frequency Region Localization Using YOLO

    Sep 3, 2025Po-Heng Chou, Wei-Lung Mao, Ru-Ping Lin +2Fault DetectionYou Only Look Once

    Research Center for Information Technology Innovation (CITI), Academia Sinica (AS), Taipei 11529, Taiwan · Department of Electrical Engineering, National Yunlin University of Science and Technology (NYUST), Yunlin 64002, Taiwan