A transformer–XGBoost based model to fault diagnosis for CPR1000

Abstract With the rapid advancement of artificial intelligence (AI), intelligent diagnostics have seen broad application across industries. To address the limitations of traditional data-driven methods in accurately identifying faults in nuclear power reactor systems, this study proposes a hybrid mo...

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Bibliografiska uppgifter

Huvudupphov:	Zhiqiang Peng, Jichong Lei, Zining Ni, Muhammad Abdul Wasaye, Yi Jiang, Yuhan Cao, Tao Yu
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	Nature Portfolio 2026-02-01
Serie:	Scientific Reports
Ämnen:	Fault diagnosis WOA Transformer XGBoost 10-fold cross-validation
Länkar:	https://doi.org/10.1038/s41598-026-38211-5
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