arXiv 2409.176042025-06-12cs.LG

RmGPT：一种用于旋转机械故障诊断与预测的生成式预训练Transformer基础模型

RmGPT: A Foundation Model with Generative Pre-trained Transformer for Fault Diagnosis and Prognosis in Rotating Machinery

School of Aeronautics and Astronautics, Shanghai Jiao Tong University（航空宇航学院，上海交通大学）；Shanghai Innovation Institute（上海创新研究院）；MINES Paris-PSL University（巴黎-里昂商学院）；Energy Department, Politecnico di Milano（能源部门，米兰理工学院）；Beijing Aeronautical Technology Research Center（北京航空航天科技研究所）

Yilin Wang, Yifei Yu, Kong Sun, Peixuan Lei, Yuxuan Zhang, Enrico Zio, Aiguo Xia, Yuanxiang Li

备注：: This paper has been accepted for publication in the IEEE Internet of Things Journal (IoT-J). The final version may differ slightly due to editorial revisions. Please cite the journal version when available

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AI总结：

针对现有PHM方法泛化性不足的问题，提出基于生成式令牌框架的RmGPT统一模型，通过自监督预训练和提示学习适配任务，在诊断、预测及少样本场景中均取得优异性能，为旋转机械PHM提供高泛化的基础模型。

摘要：

在工业领域，旋转机械的可靠性对生产效率和安全至关重要。当前的预测与健康管理（PHM）方法通常依赖于任务特定模型，在处理具有不同信号特征、故障模式和运行条件的多样化数据集时面临重大挑战。受生成式预训练模型进展的启发，我们提出了RmGPT，一种用于诊断和预测任务的统一模型。RmGPT引入了一种新颖的基于生成式令牌的框架，整合了信号令牌（Signal Tokens）、提示令牌（Prompt Tokens）、时频任务令牌（Time-Frequency Task Tokens）和故障令牌（Fault Tokens），以在统一的模型架构内处理异构数据。我们利用自监督学习实现鲁棒的特征提取，并提出了一种下一个信号令牌预测的预训练策略，同时采用高效的提示学习实现任务特定适配。大量实验表明，RmGPT显著优于最先进的算法，在诊断任务中达到近乎完美的准确率，在预测任务中实现极低的误差。值得注意的是，RmGPT在少样本学习场景中表现出色，在16类单样本实验中达到82%的准确率，凸显了其适应性和鲁棒性。这项工作确立了RmGPT作为一种强大的旋转机械PHM基础模型的地位，提升了PHM解决方案的可扩展性和泛化性。代码可在以下网址获取：https://github.com/Pandalin98/RmGPT。

英文摘要：

In industry, the reliability of rotating machinery is critical for production efficiency and safety. Current methods of Prognostics and Health Management (PHM) often rely on task-specific models, which face significant challenges in handling diverse datasets with varying signal characteristics, fault modes and operating conditions. Inspired by advancements in generative pretrained models, we propose RmGPT, a unified model for diagnosis and prognosis tasks. RmGPT introduces a novel generative token-based framework, incorporating Signal Tokens, Prompt Tokens, Time-Frequency Task Tokens and Fault Tokens to handle heterogeneous data within a unified model architecture. We leverage self-supervised learning for robust feature extraction and introduce a next signal token prediction pretraining strategy, alongside efficient prompt learning for task-specific adaptation. Extensive experiments demonstrate that RmGPT significantly outperforms state-of-the-art algorithms, achieving near-perfect accuracy in diagnosis tasks and exceptionally low errors in prognosis tasks. Notably, RmGPT excels in few-shot learning scenarios, achieving 82\% accuracy in 16-class one-shot experiments, highlighting its adaptability and robustness. This work establishes RmGPT as a powerful PHM foundation model for rotating machinery, advancing the scalability and generalizability of PHM solutions. \textbf{Code is available at: https://github.com/Pandalin98/RmGPT.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。