arXiv Machine Learning

Text-Informed Training Data Filtering for Enhanced Virtual Flow Meter Performance

The paper introduces a text-informed training data filtering approach that uses large language models to combine unstructured operational text with tabular sensor data for anomaly detection and dataset masking. Applied to a virtual flow meter for oil and gas, the multimodal method reduces mean absolute percentage error from 7.1% to 5.4% compared to sensor-only filtering. Ablation studies confirm that operator comments provide additional filtering signals beyond sensor data alone.

arXiv AI

Sep 10

Explainable Token-level Noise Filtering for LLM Fine-tuning Datasets

The paper introduces XTF, an explainable token‑level noise filtering framework for fine‑tuning large language models. XTF breaks down token contributions into reasoning importance, knowledge novelty, and task relevance, scores them, and masks gradients of noisy tokens to improve fine‑tuning. Experiments on math, code, and medicine tasks across seven LLMs show up to a 13.7% performance boost over standard fine‑tuning.

By Yuchen Yang, Wenze Lin, Enhao Huang, Zhixuan Chu, Hongbin Zhou, Lan Tao, Yiming Li, Zhan Qin, Kui Ren

llms fine-tuning benchmarks

arXiv AI

Sep 10

IPM-FM: A Foundation Model with Consensus Feature Selection for Industrial Process Monitoring

arXiv:2609. 08375v1 Announce Type: cross Abstract: Industrial process monitoring is fundamental to the safety and economic performance of modern process plants.

By Liang Cao, Weide Liu, Yan Qin, Jun Cheng, Weisi Lin, Bhushan Gopaluni

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

Jul 9

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting

arXiv:2607. 06623v1 Announce Type: cross Abstract: Process industries rely on time-series forecasting and soft sensing to estimate quality variables that are hard to measure online.

By Youcheng Zong, Runda Jia, Mingxuan Ren, Dakuo He

llms safety

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 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 AI

5d ago

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

arXiv:2610.27891v1 Announce Type: cross Abstract: Industrial fluid transport systems are monitored through heterogeneous sensing streams, but robust fault diagnosis remains difficult because response...

By Yu Sha, Junqi Tao, Dixin Zhou, Horst Stoecker, Domagoj Vnucec, Andreas Widl, Kai Zhou

benchmarks safety

arXiv AI

Jun 9

Toward autocorrection of chemical process flowsheets using large language models

arXiv:2312. 02873v2 Announce Type: replace-cross Abstract: The process engineering domain widely uses Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (P&IDs) to represent process flows and equipment configurations.

By Lukas Schulze Balhorn, Marc Caballero, Artur M. Schweidtmann

llms 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

Oct 15

Coding LLMs Can Be Harnessed to Learn Interpretable Models from Data

arXiv:2610.14006v1 Announce Type: cross Abstract: Since compact, interpretable classifiers correspond to simple programs, it is natural to ask if LLM-based coding agents can be used to learn interpre...

By Aditya Kumar, Cassandra A. Cohen, William W. Cohen

llms agents benchmarks

arXiv Machine Learning

Jun 5

Towards Unified and Data-Efficient Prognostics and Health Management with Tabular Foundation Models

arXiv:2606. 05481v1 Announce Type: new Abstract: Data-driven Prognostics and Health Management (PHM) uses time-varying condition-monitoring data to diagnose system states and estimate remaining useful life in engineered assets.

By Raffael Theiler, Lev Telyatnikov, Leandro Von Krannichfeldt, Olga Fink

llms

arXiv Machine Learning

Sep 14

Scaling Online Complex Event Detection with Synthetic Supervision and Mamba-Based Neural Algorithmic Reasoning

The paper presents NAROCE, a Neural Algorithmic Reasoning framework for online complex event detection (CED). It decouples rule learning from sensor semantics by pretraining a Mamba-based rule reasoner on synthetic atomic event traces and then adapting it to raw sensor inputs with limited labeled data. Experiments on a simulator‑generated benchmark show that NAROCE matches or surpasses strong baselines while using far fewer labeled sequences and computational resources.

By Liying Han, Gaofeng Dong, Xiaomin Ouyang, Kang Yang, Lance Kaplan, Federico Cerutti, Mani Srivastava

diffusion multimodal benchmarks

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