Fan Zhenfang, Shen Yongtao, Du Yupeng, Huang Jinying, Liu Siyuan
School of Mechanical Engineering, North University of China, Taiyuan 030051, China.
School of Data Science and Technology, North University of China, Taiyuan 030051, China.
Sensors (Basel). 2025 Aug 29;25(17):5351. doi: 10.3390/s25175351.
Blade tip timing (BTT) is a core technique for investigating the blade vibration of large-scale centrifugal compressors, and identifying the parameters of blade asynchronous vibration is crucial for implementing blade condition monitoring based on the BTT technique. In this study, the multiple signal classification (MUSIC) algorithm and the estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm were first applied separately to identify the asynchronous vibration parameters of centrifugal compressor blades, with their advantages and disadvantages discussed. Subsequently, based on the frequency distribution characteristics in the ESPRIT results, the concept of "frequency distribution rate" was proposed. Finally, the results of the MUSIC algorithm were weighted by the frequency distribution rate, and an improved MUSIC algorithm was proposed. This enhanced confidence in the real frequency in the MUSIC algorithm results. Compared with the strain gauge method, the maximum relative error of the improved algorithm is 0.23%. The improved MUSIC algorithm improves the accuracy of parameter identification for blade asynchronous vibration, which holds great significance for the industrial application of the BTT technique.
叶片叶尖定时（BTT）是研究大型离心压缩机叶片振动的一项核心技术，而识别叶片异步振动参数对于基于BTT技术实施叶片状态监测至关重要。在本研究中，首先分别应用多重信号分类（MUSIC）算法和旋转不变技术估计信号参数（ESPRIT）算法来识别离心压缩机叶片的异步振动参数，并讨论了它们的优缺点。随后，根据ESPRIT结果中的频率分布特性，提出了“频率分布率”的概念。最后，用频率分布率对MUSIC算法的结果进行加权，提出了一种改进的MUSIC算法。这增强了对MUSIC算法结果中真实频率的置信度。与应变片法相比，改进算法的最大相对误差为0.23%。改进的MUSIC算法提高了叶片异步振动参数识别的准确性，对BTT技术的工业应用具有重要意义。
