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High-Speed Blade Vibration Identification Method Based on Adaptive Superlet Transform
ID：97 View Protection：ATTENDEE Updated Time：2025-11-10 15:18:17 Hits：82 Poster Presentation

Start Time：Pending（Asia/Shanghai）

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Abstract
轴流式压缩机和烟气轮机是催化裂化装置的核心部件，叶片断裂通常由叶片共振引起，可能导致重大损失。针对高噪声工况下从受干扰或缺失的BTT信号中恢复叶片振动信息、准确识别谐振事件等挑战，提出了一种基于叶尖定时（BTT）的信号重建与谐振事件识别方法。首先，采用自适应动态阈值方法对BTT脉冲信号进行预处理，有效消除噪声和缺失数据的影响;然后，应用自适应超态变换（ASLT）进行时频分析，精确提取混叠频率并定位谐振事件。数值模拟、小规模实验装置和实际工业压缩机试验结果表明，所提方法能够从缺失信号中充分恢复叶片振动趋势，并在共振特征弱、噪声强的场景下准确识别振动幅值和频率。它成功地捕捉到了传统叶尖振动监测方法经常遗漏的共振点，增强了BTT技术的有效性，为涡轮机的安全运行提供了技术保障。
Keywords
blade tip timing, adaptive superlet transform, resonance event identification
Speaker
Wei-Min Wang
Dean of the School o Beijing University of Chemical Technology

Submission Author
Wei-Min Wang Beijing University of Chemical Technology
Ming-Long Li Beijing University of Chemical Technology
An-Ting Wei Beijing University of Chemical Technology
Jia-Wei Zhao Beijing University of Chemical Technology
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Important Date

  • Conference Date

    Nov 21

    2025

    to

    Nov 23

    2025

  • Oct 20 2025

    Draft paper submission deadline

  • Dec 08 2025

    Registration deadline

Sponsored By
IEEE Instrumentation and Measurement Society
South China University of Technology
Organized By
South China University of Technology
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