arXiv Machine Learning

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

arXiv:2606. 24954v1 Announce Type: new Abstract: Vibration-based health monitoring of rotating machinery requires reliable fault diagnosis under operational data constraints, yet condition assessment remains challenged by structural scarcity of fault events and heterogeneous sim-to-real gaps in digital twin-generated signals.

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Jun 23

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

Vibration-based health monitoring of rotating machinery requires reliable fault diagnosis under operational data constraints, yet condition assessment remains challenged by structural scarcity of fault events and heterogeneous sim-to-real gaps in digital twin-generated signals. Each fault type generates impulses with distinct periodicity, amplitude modulation, and spectral character, making feature-space discrepancies fundamentally heterogeneous across fault classes.

reinforcement-learning robotics fine-tuning safety

arXiv AI

Aug 5

Target-Aligned Fusion for Decision-Sequence Learning under Dynamics Shift

arXiv:2511. 09173v3 Announce Type: replace-cross Abstract: External trajectories can improve offline decision-sequence learning, but dynamics shift may make some source subsequences inconsistent with the target environment.

By Guojian Wang, Quinson Hon, Xuyang Chen, Lin Zhao

llms reinforcement-learning safety

arXiv Machine Learning

Jun 24

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

arXiv:2606. 24459v1 Announce Type: new Abstract: Bearing fault diagnosis faces critical challenges when dataset heterogeneity, operating condition variations, and limited labeled data occur simultaneously in industrial environments.

By Jinghan Wang, Feng Cheng, Wentao Wu, Hang Li, Gaoliang Peng, Tianchen Liu

llms rag fine-tuning benchmarks safety

arXiv AI

Jun 26

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

arXiv:2606. 26317v1 Announce Type: cross Abstract: Accurate fault diagnosis of rolling element bearings in rotating machinery is considered essential for ensuring industrial safety and enabling predictive maintenance.

By Rajeev Kumar

benchmarks safety

arXiv Machine Learning

Jul 7

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

arXiv:2509. 22267v5 Announce Type: replace Abstract: Reliable detection of bearing faults is essential for maintaining the safety and operational efficiency of rotating machinery.

By Jo\~ao Paulo Vieira, Victor Afonso Bauler, Rodrigo Kobashikawa Rosa, Danilo Silva

safety

arXiv AI

Jul 21

Multi-modal cross-domain mixed fusion model with dual disentanglement for fault diagnosis under unseen working conditions

arXiv:2512. 24679v2 Announce Type: replace Abstract: Intelligent fault diagnosis has become an indispensable technique for ensuring machinery reliability.

By Pengcheng Xia, Yixiang Huang, Chengjin Qin, Chengliang Liu

fine-tuning multimodal benchmarks

arXiv AI

Jun 11

Reliability-Calibrated Edge-IoT Early Fault Warning for Rotating Machinery with a Physics-Guided Tiny-Mamba Transformer

arXiv:2601. 21293v3 Announce Type: replace-cross Abstract: Industrial Internet of Things (IIoT) systems increasingly rely on distributed vibration sensing to support predictive maintenance of rotating machinery.

By Changyu Li, Huabei Nie, Xiaoya Ni, Lu Wang, Lijuan Shen, Kaishun Wu, Fei Luo

llms

arXiv Machine Learning

Jul 7

Modular Foundation Models for Time-Series Perception in Digital Twins

arXiv:2607. 03585v1 Announce Type: new Abstract: Engineering Digital Twins and Prognostics and Health Management (PHM) systems rely on robust perception modules to extract actionable information from heterogeneous and non-stationary time-series data.

By Quang Hung Pham, Ryad Zemouri, Martin Gagnon, Luc Vouligny

llms diffusion benchmarks safety

arXiv Machine Learning

Jul 23

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

arXiv:2607. 19455v1 Announce Type: new Abstract: In bearing vibration datasets, most samples receive predicted fault probabilities close to 0 or 1, while samples with intermediate (gray-zone) probabilities are rare.

By Seyed Mohammadreza Alavi, Ardeshir Shojaeinasab, Reza Jalayer, Masoud Jalayer, Behnam Bahrak

diffusion safety

arXiv Machine Learning

Jun 5

Two-Way Is Better Than One: Bidirectional Alignment with Cycle Consistency for Exemplar-Free Class-Incremental Learning

arXiv:2606. 05675v1 Announce Type: new Abstract: Continual learning (CL) seeks models that acquire new skills without erasing prior knowledge.

By Hongye Xu, Bartosz Krawczyk

rag benchmarks safety

arXiv AI

Jun 9

Trait-space Monitoring for Emergent Misalignment During Supervised Finetuning

arXiv:2606. 07631v1 Announce Type: cross Abstract: Emergent misalignment (EM) occurs when narrow finetuning causes a model to behave dangerously outside the finetuning task.

By Huy Nghiem, Sy-Tuyen Ho, Sarah Wiegreffe, Hal Daum\'e III

llms fine-tuning safety

arXiv AI

Aug 7

Spectral Aliasing Pretext: A novel task for Self-Supervised fault diagnosis in rotating machinery

arXiv:2608. 05705v1 Announce Type: cross Abstract: Deep learning is a new way for machinery fault diagnosis but requires extensive labeled data, a scarce resource in industrial settings.

By Victor Gialis, Maxime Metz, David Esteve, Abdenour Soualhi

llms fine-tuning

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