For data-constrained, complex and dynamic industrial environments, there is a critical need for transferable and multimodal methodologies to enhance anomaly detection and therefore, prevent costs associated with system failures. Typically, traditional PdM approaches are not transferable or multimodal. This work examines the use of Large Language Models (LLMs) for anomaly detection in complex and dynamic manufacturing systems. The research aims to improve the transferability of anomaly detection models by leveraging Large Language Models (LLMs) and seeks to validate the enhanced effectiveness of the proposed approach in data-sparse industrial applications. The research also seeks to enable more collaborative decision-making between the model and plant operators by allowing for the enriching of input series data with semantics. Additionally, the research aims to address the issue of concept drift in dynamic industrial settings by integrating an adaptability mechanism. The literature review examines the latest developments in LLM time series tasks alongside associated adaptive anomaly detection methods to establish a robust theoretical framework for the proposed architecture. This paper presents a novel model framework (AAD-LLM) that doesn't require any training or finetuning on the dataset it is applied to and is multimodal. Results suggest that anomaly detection can be converted into a "language" task to deliver effective, context-aware detection in data-constrained industrial applications. This work, therefore, contributes significantly to advancements in anomaly detection methodologies.

翻译：在数据受限、复杂且动态的工业环境中，亟需具备可迁移性和多模态特性的方法来增强异常检测能力，从而避免系统故障带来的损失。传统的预测性维护方法通常不具备可迁移性或多模态特性。本研究探讨了在复杂动态制造系统中利用大型语言模型进行异常检测的可行性。研究旨在通过利用大型语言模型提升异常检测模型的可迁移性，并验证所提方法在数据稀疏的工业应用中的增强有效性。研究还试图通过为输入序列数据赋予语义信息，促进模型与工厂操作人员之间更协同的决策制定。此外，研究通过集成自适应机制，致力于解决动态工业环境中的概念漂移问题。文献综述梳理了LLM在时间序列任务中的最新进展以及相关的自适应异常检测方法，为所提出的架构建立了坚实的理论基础。本文提出了一种新颖的模型框架（AAD-LLM），该框架无需在其应用的数据集上进行任何训练或微调，且具备多模态能力。结果表明，异常检测可转化为一种“语言”任务，从而在数据受限的工业应用中实现有效且具有上下文感知能力的检测。因此，本研究对异常检测方法的进步做出了重要贡献。