arXiv AI

Physical-Variable-Informed Deep Learning for Robust Diagnosis in Industrial Fluid Transport Systems

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 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 Machine Learning

Sep 1

Geometric Attractor Monitoring: A Robust and Frugal Framework for Multi-modal Industrial Robotic Cycles

arXiv:2608.30804v1 Announce Type: new Abstract: Monitoring the health of heterogeneous industrial robot fleets is severely challenged by the multi-modal nature of their operational cycles and a persi...

By Martin Bonsergent-Brachet, Jesse Read, Dany Abboud

robotics multimodal safety

arXiv Machine Learning

Jun 19

Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty

arXiv:2412. 18980v2 Announce Type: replace Abstract: Uncertainty-aware deep learning (DL) models recently gained attention in fault diagnosis as a way to promote the reliable detection of faults when out-of-distribution (OOD) data arise from unseen faults (epistemic uncertainty) or the presence of noise (aleatoric uncertainty).

By Reza Jalayer, Masoud Jalayer, Andrea Mor, Carlotta Orsenigo, Carlo Vercellis

benchmarks

arXiv Machine Learning

Jun 19

PaAno+: Multiscale Encoding and Cross-Variable Attention for Time Series Anomaly Detection

arXiv:2606. 20055v1 Announce Type: new Abstract: Time-series anomaly detection has significant practical value for industrial and medical monitoring, as well as other critical domains.

By Youji Zhu, Hongbing Wang, Wenchao Liu, Xiaodong Liu, Xiangguang Xiong

llms rag benchmarks

arXiv Machine Learning

Oct 7

A Data-Driven Framework for Unsupervised Monitoring of Transmission Systems Using End-of-Line Testing Data: A Case Study at Ford Motor Company

The paper presents a two‑stage multivariate monitoring framework for high‑dimensional, temporally dependent data from end‑of‑line testing in automotive manufacturing. Stage one cleans and aligns the data, while stage two applies nonlinear dimensionality reduction and a control‑chart‑based anomaly detection procedure that can operate in unsupervised or supervised modes. Applied to Ford Motor Company production data, the framework outperforms the existing model, raising accuracy from 0.50 to 0.625, recall from 0.30 to 1.00, and F1 score from 0.429 to 0.769.

By Mohammad N. Bisheh, Mehrdad Moradi, Parinaz Farajiparvar, Colin Brady, Rajesh Gupta, Xueling Li, Javad Navaei, Milad Parvaneh, Kamran Paynabar

arXiv Machine Learning

Aug 17

Deep Vision in Smart Manufacturing: MODERN Framework for Intelligent Quality Monitoring and Diagnosis

arXiv:2608. 13937v1 Announce Type: cross Abstract: Smart manufacturing processes are often installed with a large number of sensors, imaging devices and computers, which not only enable instant communication across various modules of a production system but also aid in intelligent manufacturing management.

By Yicheng Kang, Yuling Jiao, Xin Geng, Mahesh Nagarajan

fine-tuning benchmarks

arXiv Machine Learning

Jul 21

Leakage-Robust Evaluation and Data-Scale Sensitivity of Attention-Enhanced Multi-Task Learning for Joint Fault Diagnosis and Remaining Useful Life Estimation

arXiv:2607. 16493v1 Announce Type: new Abstract: Multi-task deep learning models that jointly perform fault classification and remaining useful life (RUL) regression are increasingly used in predictive maintenance, yet reported performance can be strongly affected by how sliding-window sequences are split into training and test sets.

By Md Mahamudur Rahaman Shamim, Md. Nuruzzaman, Zannatul Ferdus, Md Rajib Ahmed, Abieer Nwshad Anward, Mohammad Tooneer, Johir Uddin Khan, Khalid Hossen

benchmarks

arXiv AI

Jun 11

Physics-Distilled Neural Network enabled by Large Language Models for Manufacturing Process-Property Predictive Modeling

arXiv:2606. 11605v1 Announce Type: cross Abstract: Predicting process-property relationships in manufacturing is often challenged by high experimental costs and the limited interpretability of complex 'black-box' models.

By Ge Song, Kiarash Naghavi Khanghah, Anandkumar Patel, Rajiv Malhotra, Hongyi Xu

llms efficiency safety

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

Sep 1

Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection

arXiv:2604.09166v3 Announce Type: replace Abstract: Anomaly detection (AD) in chemical processes based on deep learning offers significant opportunities but requires large, diverse, and well-annotate...

By Jennifer Werner, Justus Arweiler, Indra Jungjohann, Jochen Schmid, Fabian Jirasek, Hans Hasse, Michael Bortz

efficiency

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

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