Towards Unlocking Insights from Logbooks Using AI

arXiv - PHYS - Accelerator Physics Pub Date : 2024-05-25 DOI:arxiv-2406.12881

Antonin Sulc, Alex Bien, Annika Eichler, Daniel Ratner, Florian Rehm, Frank Mayet, Gregor Hartmann, Hayden Hoschouer, Henrik Tuennermann, Jan Kaiser, Jason St. John, Jennefer Maldonado, Kyle Hazelwood, Raimund Kammering, Thorsten Hellert, Tim Wilksen, Verena Kain, Wan-Lin Hu

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Abstract

Electronic logbooks contain valuable information about activities and events concerning their associated particle accelerator facilities. However, the highly technical nature of logbook entries can hinder their usability and automation. As natural language processing (NLP) continues advancing, it offers opportunities to address various challenges that logbooks present. This work explores jointly testing a tailored Retrieval Augmented Generation (RAG) model for enhancing the usability of particle accelerator logbooks at institutes like DESY, BESSY, Fermilab, BNL, SLAC, LBNL, and CERN. The RAG model uses a corpus built on logbook contributions and aims to unlock insights from these logbooks by leveraging retrieval over facility datasets, including discussion about potential multimodal sources. Our goals are to increase the FAIR-ness (findability, accessibility, interoperability, and reusability) of logbooks by exploiting their information content to streamline everyday use, enable macro-analysis for root cause analysis, and facilitate problem-solving automation.

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利用人工智能从航海日志中获取启示

电子日志包含了与粒子加速器设施相关的活动和事件的宝贵信息。然而，日志条目的高度技术性可能会妨碍其可用性和自动化。随着自然语言处理（NLP）技术的不断进步，它为解决日志带来的各种挑战提供了机会。这项工作探讨了如何联合测试一种量身定制的检索增强生成（RAG）模式，以提高欧洲核子研究中心（CERN）等机构的粒子加速器日志的可用性，这些机构包括：DESY、BESSY、费米实验室（Fermilab）、BNL、SLAC、LBNL 和欧洲核子研究中心（CERN）。RAG 模型使用基于日志贡献建立的语料库，旨在通过对设施数据集的检索，包括对潜在多模态源的讨论，从这些日志中发掘见解。我们的目标是通过利用日志的信息内容来提高日志的 FAIR 性（可查找性、可访问性、可互操作性和可重用性），从而简化日常使用，实现用于根本原因分析的宏观分析，并促进问题解决自动化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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arXiv - PHYS - Accelerator Physics

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