Large language models for PHM: a review of optimization techniques and applications

Yu, Tingyi, Tang, Junya, Yu, Qingyun, Li, Li, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 and Poler, Raul 2025. Large language models for PHM: a review of optimization techniques and applications. Autonomous Intelligent Systems 5 (1) , 18. 10.1007/s43684-025-00100-5

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Abstract

The rapid advancement of Large Language Models (LLMs) has created unprecedented opportunities for industrial automation, process optimization, and decision support systems. As industries seek to leverage LLMs for industrial tasks, understanding their architecture, deployment strategies, and fine-tuning methods becomes critical. In this review, we aim to summarize the challenges, key technologies, current status, and future directions of LLM in Prognostics and Health Management(PHM). First, this review introduces deep learning for PHM. We begin by analyzing the architectural considerations and deployment strategies for industrial environments, including acceleration techniques and quantization methods that enable efficient operation on resource-constrained industrial hardware. Second, we investigate Parameter Efficient Fine-Tuning (PEFT) techniques that allow industry-specific adaptation without prohibitive computational costs. Multi-modal capabilities extending LLMs beyond text to process sensor data, images, and time-series information are also discussed. Finally, we explore emerging PHM including anomaly detection systems that identify equipment malfunctions, fault diagnosis frameworks that determine root causes, and specialized question-answering systems that empower workers with instant domain expertise. We conclude by identifying key challenges and future research directions for LLM deployment in PHM. This review provides a timely resource for researchers, engineers, and decision-makers navigating the transformative potential of language models in industry 4.0 environments.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher:	Springer Nature
ISSN:	2730-616X
Date of First Compliant Deposit:	26 August 2025
Date of Acceptance:	15 May 2025
Last Modified:	26 Aug 2025 14:15
URI:	https://orca.cardiff.ac.uk/id/eprint/180662

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