85 / 2025-05-14 21:14:34
DiagLLM: A CNN-Language Model Framework with LoRA for Fault Diagnosis of Cross Roller Bearings
Fault Diagnosis,Cross Roller Bearings,Large Language Models,CNN,LoRA
全文待审
Jing Liu / South China University of Technology
Jiaxian Chen / South China University of Technology
Shuhan Deng / South China University of Technology
Kairu Wen / South China University of Technology
Guolin He / South China University of Technology
Weihua Li / South China University of Technology
Cross roller bearings are vital in precision mechanical systems, where early-stage faults can lead to severe operational failures. While traditional deep learning method, such as CNNs and autoencoders, have shown promise in vibration-based fault diagnosis, their limited semantic modeling capacity restricts robustness under noisy or data-scarce conditions. To address this challenge, a novel diagnostic framework, named DiagLLM, is proposed, in which low-dimensional features extracted by a 1D CNN are embedded into a pre-trained large language model (LLaMA), enabling semantic-level classification through parameter-efficient LoRA tuning. Unlike prior approaches that require handcrafted prompts or domain-specific adaptations, DiagLLM enables direct and lightweight integration of sensor signals into transformer architectures. A comprehensive experimental study is performed on a cross roller bearing test rig, involving diverse feature representations and backbone architectures to systematically assess the diagnostic performance. The results show that DiagLLM consistently outperforms conventional methods in both full-data and few-shot scenarios, demonstrating its effectiveness and adaptability for intelligent fault diagnosis in industrial settings. This work not only enables lightweight integration of sensor signals with LLMs, but also establishes a novel and generalizable paradigm for semantic-level diagnostics in data-scarce and noisy environments.
重要日期
  • 会议日期

    08月01日

    2025

    08月04日

    2025

  • 06月15日 2025

    初稿截稿日期

主办单位
中国机械工程学会设备智能运维分会
承办单位
新疆大学
移动端
在手机上打开
小程序
打开微信小程序
客服
扫码或点此咨询