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Gas Generator Blade Fracture Early Warning Method Based on BPIP-OT Feature Reconstruction
编号：100 访问权限：仅限参会人 更新：2025-11-10 15:24:05 浏览：13次 张贴报告

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摘要
Fracture failures in gas generator blades due to factors such as high-cycle fatigue, hot corrosion, rubbing, and foreign object damage occur periodically. Based on the mechanism of blade fracture fault in casing vibration signals, this study proposes a weak fault feature extraction and early warning methodology to support blade condition monitoring in turbine units. Firstly, based on blade fracture vibration response characteristics, the impact of minor rotational speed variations on blade passing frequency (BPF) amplitude extraction is thoroughly investigated. A Blade Passing Frequency Instantaneous Phase-Based Order Tracking (BPIP-OT) method is proposed, enabling accurate extraction of weak blade fault features. Secondly, utilizing optimized order features as input, a blade fracture early warning model is established through BPIP-OT-based variational autoencoder (VAE),where a comprehensive reconstruction error metric integrating mean square error (MSE) and the top M largest MSE values is designed. Finally, validation is performed via a field case study of blade fracture in an industrial gas turbine. Results demonstrate:(1)Spectra processed by the BPIP-OT algorithm effectively mitigate spectral smearing induced by minor speed variation, yielding concentrated energy distribution and enhanced amplitude accuracy at BPF.(2)Compared to traditional models using original spectra as input, the BPIP-OT-based VAE model proposed herein shows higher recognition accuracy.(3)The optimized dimensionality-reduced features reduce model parameterization and training complexity, demonstrating enhanced suitability for engineering applications in monitoring and early warning of gas generator blade fracture faults.
 
关键词
Blade fracture fault, Blade passing frequency, Instantaneous phase, Variational autoencoder, Early warning, Gas generator
报告人
hao gaoyan
Senior Engineer 北京博华信智科技股份有限公司

稿件作者
hao gaoyan 北京博华信智科技股份有限公司
Yingli Li China Petroleum Safety and Environmental Protection Technology Research Institute
FENG Kun Beijing University of Chemical Technology
Peng Zhang Beijing University of Chemical Technology
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重要日期

  • 会议日期

    11月21日

    2025

    至

    11月23日

    2025

  • 10月20日 2025

    初稿截稿日期

  • 11月23日 2025

    注册截止日期

主办单位
IEEE Instrumentation and Measurement Society
South China University of Technology
承办单位
South China University of Technology
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