RmGPT: Rotating Machinery Generative Pretrained Model
<|reference_start|>RmGPT: Rotating Machinery Generative Pretrained Model: 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 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 92% 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.<|reference_end|>
@article{wang2024rmgpt:,
    title={RmGPT: Rotating Machinery Generative Pretrained Model},
    author={Yilin Wang, Yifei Yu, Kong Sun, Peixuan Lei, Yuxuan Zhang, Enrico Zio,
  Aiguo Xia, Yuanxiang Li},
    journal={arXiv preprint arXiv:2409.17604},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17604},
    primaryClass={cs.LG}
}