arXiv Machine Learning

Marine Engine Fault Dataset: Open-Access Data under Controlled Reference and Fault Scenario Conditions

arXiv:2607. 19444v1 Announce Type: new Abstract: Open-access datasets for marine-engine predictive maintenance remain scarce, particularly those from controlled fault experiments with documented operating conditions, subsystem-level interventions and system-level measurements.

arXiv AI

Jun 26

Kalman Prototypical Networks for Few-shot Fault Detection in Combined Cycle Gas Turbines

arXiv:2606. 26710v1 Announce Type: new Abstract: Combined-cycle gas turbines (CCGTs) play a key role in modern power generation, offering both high efficiency and reduced environmental impact.

By Mohammed Ayalew Belay, Lucas Ferreira Bernardino, Adil Rasheed, Rub\'en M. Monta\~n\'es, Pierluigi Salvo Rossi

rag

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 5

Anomaly Detection for Electro-Hydrostatic Actuators using LSTM Autoencoder

arXiv:2606. 05274v1 Announce Type: new Abstract: Electro-Hydrostatic Actuators (EHAs) are widely used in aerospace and industrial systems, where timely detection of sensor anomalies is essential to ensure safe and reliable operation.

By Nehal Afifi, Abdelmonem Elhendawi, Felix Leitenberger, Nadine Piat, Sven Matthiesen

arXiv AI

Jul 28

Physically Verifiable Evidence and LLM-Based Reporting for Bearing Fault Diagnosis

arXiv:2607. 22797v1 Announce Type: cross Abstract: Trustworthy deployment of AI-based diagnosis in safety-critical mechanical systems hinges on validation: whether a prediction can be checked against physical reality before it is acted upon.

By Yuntong Chen, Jianyu Liu, Guobin Zhao, Ziang Wang, Chao Chen, Ju Huang, Xitian Tian, Lijiang Huang

llms fine-tuning safety

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 30

Faults in Our Formal Benchmarking: Dataset Defects and Evaluation Failures in Lean Theorem Proving

arXiv:2606. 29493v1 Announce Type: new Abstract: Benchmarks for LLM-assisted theorem proving in Lean are often treated as intrinsically reliable because every solved instance comes with a machine-checked proof.

By Pawan Sasanka Ammanamanchi, Siddharth Bhat, Stella Biderman

llms benchmarks safety

arXiv Machine Learning

Jun 18

TS-Fault: Benchmarking Time Series Forecasters Against Structural Faults

arXiv:2606. 18539v1 Announce Type: new Abstract: Time series forecasting (TSF) underpins consequential decisions in energy, transportation, finance, and healthcare, yet TSF models are almost universally ranked by a single number (e.

By Yuyang Zhao, Lian Xu, Hao Miao, Chenxi Liu, Hao Xue

benchmarks

arXiv AI

2d ago

Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoring

arXiv:2608. 14666v1 Announce Type: new Abstract: Unsupervised fault detection in industrial systems is dominated by reconstruction based methods that monitor individual sensor marginal distributions.

By Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley, Sunil Aryal

rag multimodal safety

arXiv AI

Aug 6

Robust and Personalized Federated Learning for Aircraft-Engine Prognostics under Benign and Adversarial Client Heterogeneity

arXiv:2608. 04045v1 Announce Type: cross Abstract: Federated learning (FL) enables aircraft fleet operators to jointly train remaining-useful-life (RUL) models from engine sensor telemetry without sharing raw data.

By Chinmoy Mitra, Md. Mehedi Hasan Nipu, Mohammad Sakib Mahmood, Md. Rakibul Islam, M. F. Mridha

benchmarks safety

arXiv Machine Learning

Jul 3

Liquid Latent State Dynamics for Interpretable Turbofan Degradation Modeling

arXiv:2607. 01986v1 Announce Type: new Abstract: Multivariate time-series models for prognostics are often evaluated by point prediction accuracy, yet their internal states rarely expose a coherent degradation process.

By Weizhi Nie, Weijie Wang, Yuting Su

benchmarks

arXiv Machine Learning

Jul 23

CruiseBench: A Real-Flight-Aligned N-CMAPSS Benchmark for Engine RUL Prediction

arXiv:2607. 19380v1 Announce Type: new Abstract: Remaining useful life (RUL) prediction estimates how long an engine can continue safe operation and is central to maintenance planning.

By Pu Cheng, Qiang Miao

benchmarks

arXiv Machine Learning

Jul 15

Deep4ge: DNN Training Trajectories for Fault Detection and Diagnosis

arXiv:2607. 12868v1 Announce Type: cross Abstract: Deep learning systems often fail due to subtle implementation faults that alter training behavior.

By Sigma Jahan

benchmarks

Related stories