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KG-SR-LLM: Knowledge-Guided Semantic Representation and Large Language Model Framework for Cross-Domain Bearing Fault

Chengyong Xiao1, Xiaowei Liu2, Aziguli Wulamu2

  • 1School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.

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|September 27, 2025
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Summary

This study introduces a new method using Large Language Models (LLMs) for generalized bearing fault diagnosis. The Knowledge-Guided Semantic Representation and Large Language Model (KG-SR-LLM) achieves 98.36% accuracy, outperforming traditional models.

Keywords:
Large Language Modelbearing fault diagnosiscross-domain generalizationdomain knowledge fusionknowledge-guided fine-tuning

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Area of Science:

  • Mechanical Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Bearing fault diagnosis is vital for industrial operations but faces challenges with cross-domain generalization due to complex vibration modes and conditions.
  • Existing methods struggle to adapt to diverse operating environments and non-linear vibration patterns, limiting their practical application.

Purpose of the Study:

  • To develop a generalized diagnostic framework for bearing fault diagnosis that enhances accuracy and adaptability across different domains and conditions.
  • To leverage Large Language Models (LLMs) for improved interpretation of vibration data and integration of expert knowledge.

Main Methods:

  • A structured representation approach converts vibration time series into text sequences by extracting time and frequency domain features.
  • A knowledge-guided prompt tuning strategy (LoRA-Prompt) integrates bearing structural parameters and operating condition information.
  • A novel Knowledge-Guided Semantic Representation and Large Language Model (KG-SR-LLM) method is established for generalized fault diagnosis.

Main Results:

  • The KG-SR-LLM method achieved an average diagnostic accuracy of 98.36% on 11 public datasets from industrial, aerospace, and energy fields.
  • KG-SR-LLM demonstrated superior performance compared to classical deep learning models, with a 9.22% improvement.
  • The framework proved effective in few-shot transfer learning and cross-condition adaptation tasks.

Conclusions:

  • The KG-SR-LLM framework offers a significant advancement in intelligent bearing fault diagnosis, overcoming limitations of existing methods.
  • The approach provides theoretical significance and practical benefits for reliable and adaptable fault diagnosis in industrial settings.
  • The integration of LLMs with structured data representation and knowledge guidance enables robust cross-domain generalization.