RT Journal Article
SR Electronic
A1 Cheng, Fengqin
A1 Zheng, Weinan
T1 Research on Stator Thermal Fault Detection of Steam Turbine Generator Based on Improved Transformer and Gaussian Mixture Model
JF Manufacturing Technology Journal
YR 2025
VO 25
IS 4
SP 448
OP 454
DO 10.21062/mft.2025.051
UL https://journalmt.com/artkey/mft-202504-0006.php
AB This study proposes a multi-stage intelligent diagnostic approach integrating Physics-Guided Normalization (LPGN), enhanced Transformer networks, and Gaussian Mixture Models (GMM) for thermal fault detection in turbine generator stators. The methodology sequentially performs the following steps: (1) enhances localized anomaly features in temperature data through LPGN, (2) efficiently extracts temporal patterns via the optimized Transformer architecture, and (3) achieves unsupervised fault classification using GMM. Experimental results demonstrate the proposed method's superiority over conventional ARIMA and LSTM models across multiple evaluation metrics, exhibiting a lower RMSE and a higher detection accuracy. Ablation studies further validate the individual contributions of each component to performance improvement. This solution provides an efficient and reliable framework for intelligent thermal monitoring in large rotating electrical machinery.