PT  - JOURNAL ARTICLE
AU  - Cheng, Fengqin
AU  - Zheng, Weinan
TI  - Research on Stator Thermal Fault Detection of Steam Turbine Generator Based on Improved Transformer and Gaussian Mixture Model
DP  - 2025 Nov 11
TA  - Manufacturing Technology Journal
PG  - 448--454
VI  - 25
IP  - 4
AID - 10.21062/mft.2025.051
IS  - 12132489
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.