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A virtual sample diffusion generation method guided by LLM-generated knowledge for enhancing information completeness and zero-shot fault diagnosis in building thermal systems
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Zhe SUN1, Qiwei YAO1, Ling SHI1, Huaqiang JIN3, Yingjie XU1, Peng YANG1, Han XIAO1, Dongyu CHEN4, PanPan ZHAO5, Xi SHEN1,2. A virtual sample diffusion generation method guided by LLM-generated knowledge for enhancing information completeness and zero-shot fault diagnosis in building thermal systems[J]. Journal of Zhejiang University Science A, 1998, -1(-1): .
@article{title="A virtual sample diffusion generation method guided by LLM-generated knowledge for enhancing information completeness and zero-shot fault diagnosis in building thermal systems",
author="Zhe SUN1, Qiwei YAO1, Ling SHI1, Huaqiang JIN3, Yingjie XU1, Peng YANG1, Han XIAO1, Dongyu CHEN4, PanPan ZHAO5, Xi SHEN1,2",
journal="Journal of Zhejiang University Science A",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2400560"
}
%0 Journal Article
%T A virtual sample diffusion generation method guided by LLM-generated knowledge for enhancing information completeness and zero-shot fault diagnosis in building thermal systems
%A Zhe SUN1
%A Qiwei YAO1
%A Ling SHI1
%A Huaqiang JIN3
%A Yingjie XU1
%A Peng YANG1
%A Han XIAO1
%A Dongyu CHEN4
%A PanPan ZHAO5
%A Xi SHEN1
%A 2
%J Journal of Zhejiang University SCIENCE A
%V -1
%N -1
%P
%@ 1673-565X
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2400560
TY - JOUR
T1 - A virtual sample diffusion generation method guided by LLM-generated knowledge for enhancing information completeness and zero-shot fault diagnosis in building thermal systems
A1 - Zhe SUN1
A1 - Qiwei YAO1
A1 - Ling SHI1
A1 - Huaqiang JIN3
A1 - Yingjie XU1
A1 - Peng YANG1
A1 - Han XIAO1
A1 - Dongyu CHEN4
A1 - PanPan ZHAO5
A1 - Xi SHEN1
A1 - 2
J0 - Journal of Zhejiang University Science A
VL - -1
IS - -1
SP -
EP -
%@ 1673-565X
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2400560
Abstract: In the era of big data, data-driven technologies are increasingly leveraged by industry to facilitate autonomous learning and intelligent decision-making. However, the challenge of “small samples in big data” emerges when datasets lack comprehensive information necessary for addressing complex scenarios, which hampers adaptability. Thus, enhancing data completeness is essential. knowledge-guided virtual sample generation transforms domain knowledge into extensive virtual datasets, thereby reducing de-pendence on limited real samples and enabling zero-sample fault diagnosis. This study used building air conditioning systems as a case study. We innovatively used the LLM model to acquire domain knowledge for sample generation, significantly lowering knowledge acquisition costs and establishing a generalized framework for knowledge acquisition in engineering applications. This acquired knowledge guided the design of diffusion boundaries in mega-trend diffusion, while the Monte Carlo method was used to sample within the diffusion function to create information-rich virtual samples. Additionally, a noise-adding technique was introduced to enhance the information entropy of these samples, thereby improving the robustness of neural networks trained with them. Exper-imental results showed that training the diagnostic model exclusively with virtual samples achieved an accuracy of 72.80%, signifi-cantly surpassing traditional small-sample supervised learning in terms of generalization. This underscores the quality and com-pleteness of the generated virtual samples.
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