arXiv AI September 24, 2026 By Igor Bezmaternykh (INSA Toulouse), Louise Trav\'e-Massuy\`es (LAAS-DISCO, Comue de Toulouse, ANITI), Elodie Chanthery (LAAS)

Discovery of fully efficient fault indicators along a data-based diagnosis process

Read the original on arXiv AI →

The paper presents DT4X+, an improved diagnosis algorithm that builds on DT4X by refining training set construction and symbolic‑regression loss to better separate target classes while maintaining coherence among non‑target classes. This results in relations that fully align with analytical redundancy relation properties, yielding more informative decision‑tree splits, enhanced robustness, and superior performance on dynamic‑system datasets. Experiments on benchmark systems confirm these advantages.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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

Aug 28

Learning to Predict, Discover, and Reason in High-Dimensional Event Sequences

The paper proposes a new framework for automated fault diagnostics in modern vehicles by treating diagnostic trouble codes (DTCs) as a high‑dimensional language. It introduces Transformer‑based models for predictive maintenance, scalable causal discovery methods, and a multi‑agent system that automatically generates Boolean error‑pattern rules. The approach aims to replace costly manual grouping of DTCs with scalable, data‑driven techniques.

By Hugo Math

llms agents safety

arXiv Machine Learning

Jun 5

Quantifying Sensitivity for Tree Ensembles: A symbolic and compositional approach

arXiv:2605. 13830v2 Announce Type: replace-cross Abstract: Decision tree ensembles (DTE) are a popular model for a wide range of AI classification tasks, used in multiple safety critical domains, and hence verifying properties on these models has been an active topic of study over the last decade.

By Ajinkya Naik, Chaitanya Garg, S. Akshay, Ashutosh Gupta, Kuldeep S. Meel

benchmarks safety

arXiv Machine Learning

Jun 24

Experiments with Optimal Model Trees

arXiv:2503. 12902v4 Announce Type: replace Abstract: Model trees provide an appealing way to perform interpretable machine learning for both classification and regression problems.

By Sabino Francesco Roselli, Eibe Frank

benchmarks 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 15

ANSR-DT: A Neuro-Symbolic Framework for Adaptive and Explainable Digital Twins

arXiv:2501. 08561v4 Announce Type: replace Abstract: Digital twins are increasingly used to monitor and optimize industrial systems, yet many existing frameworks remain difficult to interpret, slow to adapt, and limited in their ability to incorporate explicit domain knowledge.

By Safayat Bin Hakim, Muhammad Adil, Alvaro Velasquez, Houbing Herbert Song

reinforcement-learning benchmarks safety

One email a morning, machine-written

Related stories

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

Learning to Predict, Discover, and Reason in High-Dimensional Event Sequences

Quantifying Sensitivity for Tree Ensembles: A symbolic and compositional approach

Experiments with Optimal Model Trees

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

ANSR-DT: A Neuro-Symbolic Framework for Adaptive and Explainable Digital Twins