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Updated: Jan 29, 2026

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一个PI-双-STGCN故障诊断模型基于SHAP-LLM联合解释框架

Zheng Zhao1,2, Shuxia Ye1,2, Liang Qi1,2

  • 1School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China.

Sensors (Basel, Switzerland)
|January 28, 2026
PubMed
概括

本研究介绍了一种PI-Dual-STGCN模型与SHAP-LLM框架用于透明的工业故障诊断. 这种新的方法实现了99.22%的准确性,并使用可解释AI和检索增强生成提高了可解释性.

关键词:
在法学士 (LLM) 课程中.这就是 SHAP SHAP 的意思.可以解释性的解释性.可以解释的人工智能AI错误诊断 错误诊断 错误诊断 是一个问题.

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