BearLLM: A Prior Knowledge-Enhanced Bearing Health Management Framework with Unified Vibration Signal Representation

🔥 NEWS

• [2024-12-11] ⏫ We are now working on making the code of BearLLM public. Stay tuned!
• [2024-12-10] 🎉 The BearLLM paper is accepted by the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25).
• [2024-08-21] 📝 The preprint of the BearLLM paper is available on arXiv. Check the paper page for more details.

📅 TODO

• Upload the health management corpus of the MBHM dataset.
• Collect the codes for pre-training and fine-tuning BearLLM.
• Collect the codes of BearLLM's classification network and other comparison models.
• Upload the vibration signal portion of the MBHM dataset.

📚 Introduction

The MBHM dataset is the first multimodal dataset designed for the study of bearing health management. It is divided into two parts: vibration signals and health management corpus. The vibration signals and condition information are derived from 9 publicly available datasets, and are still under continuous updating and improvement. The thousands of working conditions pose more difficult challenges for the identification model and better represent real-world usage scenarios.

BearLLM is a prior knowledge-enhanced bearing health management framework with a unified vibration signal representation. This framework transforms the signal to be tested into the frequency domain, enabling effective identification of spectral differences compared to the vibration signal under fault-free conditions. By aligning the vibration signal with the fault semantic embedding, we achieve a unified natural language response for various health management tasks through a fine-tuned language model with low computational overhead. Experiments demonstrate that this framework achieves leading performance under thousands of working conditions.

💻 Requirements

The code is implemented in Python 3.12. The required packages are listed in the requirements.txt file. You can install the required packages by running the following command:

conda create --name bearllm python=3.12
conda activate bearllm
conda install pytorch pytorch-cuda=12.4 -c pytorch -c nvidia
pip install -r requirements.txt
pip install git+https://github.com/huggingface/transformers
pip install git+https://github.com/huggingface/peft

📖 Citation

Please cite the following paper if you use this study in your research:

@misc{peng2024bearllmpriorknowledgeenhancedbearing,
      title={BearLLM: A Prior Knowledge-Enhanced Bearing Health Management Framework with Unified Vibration Signal Representation}, 
      author={Haotian Peng and Jiawei Liu and Jinsong Du and Jie Gao and Wei Wang},
      year={2024},
      eprint={2408.11281},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2408.11281}, 
}