辽宁石油化工大学学报2026，Vol.46Issue(1)：71-80,10.DOI:10.12422/j.issn.1672-6952.2026.01.009
基于LKAN神经网络的变压器故障诊断模型研究
Research on Transformer Fault Diagnosis Model Based on LKAN Neural Network
摘要
Abstract
In transformer fault diagnosis accuracy,addressing the limitations of traditional neural networks such as insufficient interpretability and weak temporal feature extraction capabilities,this study proposes a novel diagnostic model,LKAN which integrates Long Short-Term Memory(LSTM)with Kolmogorov-Arnold Network(KAN).The model first employs LSTM to model time-series data from transformer operations,extracting hidden states as temporal features.These features are then fed into the KAN layer,where B-spline functions enable nonlinear mapping and function decomposition,thereby enhancing both the model's expressiveness and interpretability.Experimental results on real-world power transformer datasets demonstrate that the LKAN model achieves a diagnostic accuracy of 98.80%,outperforming LSTM,Convolutional Neural Network(CNN),Gated Recurrent Unit(GRU),and single KAN models.Meanwhile,it exhibits strong generalization ability and stability.The LKAN model effectively integrates the temporal modeling capability of LSTM and the interpretability advantage of KAN.It provides a technical path with high accuracy and strong interpretability for intelligent fault diagnosis of transformers,and has good engineering promotion value.关键词
变压器故障诊断/LKAN模型/LSTM/KAN/可解释性神经网络Key words
Transformer fault diagnosis/LKAN model/LSTM/KAN/Interpretable neural network分类
信息技术与安全科学引用本文复制引用
赵子天,陈帅,邱海洋..基于LKAN神经网络的变压器故障诊断模型研究[J].辽宁石油化工大学学报,2026,46(1):71-80,10.基金项目
国家自然科学基金项目(51374098). （51374098）
