Research on Stator Thermal Fault Detection of Steam Turbine Generator Based on Improved Transformer and Gaussian Mixture Model
Year: 2025
Type: article
Abstract: 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.
Source: MANUFACTURING TECHNOLOGY
Authors: Fengqin Cheng, Weinan Zheng
Institution: Jilin University of Architecture and Technology
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4415420495