Manufacturing Technology 2025, 25(4):448-454 | DOI: 10.21062/mft.2025.051

Research on Stator Thermal Fault Detection of Steam Turbine Generator Based on Improved Transformer and Gaussian Mixture Model

Fengqin Cheng ORCID..., Weinan Zheng ORCID...
College of Electrical and Mechanical Engineering, Jilin University of Architecture and Technology, Jilin, 130114, China

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.

Keywords: Stator Thermal Fault, Transformer, Gaussian Mixture Model, Fault Diagnosis
Grants and funding:

Research on the detection of nutrient elements in saline alkali soil by combining machine learning algorithms with LIBS technology (JJKH20241490KJ)

Received: May 23, 2025; Revised: September 10, 2025; Accepted: September 30, 2025; Prepublished online: October 22, 2025; Published: November 11, 2025  Show citation

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Cheng F, Zheng W. Research on Stator Thermal Fault Detection of Steam Turbine Generator Based on Improved Transformer and Gaussian Mixture Model. Manufacturing Technology. 2025;25(4):448-454. doi: 10.21062/mft.2025.051.
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