水轮机空化声发射信号的联合降噪与特征提取北大核心CSCD

探究水轮机空化诱导的声发射信号随空化状态的变化规律对监测空化具有重要意义。为解决声发射信号携带噪声和特征提取困难的问题,本文建立一种基于自适应迭代滤波分解-奇异谱分析联合降噪和固有时间尺度分解结合特征参数的特征提取方法。首先,采用自适应迭代滤波分解结合相关系数对声发射信号初步降噪,滤除明显噪声分量,重构剩余分量并通过奇异谱分析进一步降噪,将所得信号与趋势分量相加,完成整个降噪过程。然后,对降噪后信号进行固有时间尺度分解,筛选出有效分量,计算其绝对能量和相对能量熵。最后,分析它们随空化系数变化的规律。研究结果表明,绝对能量和相对能量熵随空化系数的变化具有明显的规律性,均能反映水轮机空化的发展状况。

Joint noise reduction and feature extraction of acoustic emission signals for hydraulic turbines under cavitation

Understanding the acoustic emission signals from hydraulic turbines under flow cavitation and its variations with cavitation intensity is essential for monitoring cavitation. To overcome the difficulty in feature extraction from acoustic signals due to noise pollution, this paper develops a feature extraction method based on noise reduction through adaptive local iterative filtering and singular spectrum analysis, and on intrinsic time scale decomposition combined with feature parameters. First, an acoustic signal is initially denoised using the adaptive local iterative filtering combined with correlation coefficients to filter out evident noise components; the remaining components are reconstructed and further denoised via singular spectrum analysis. Adding the resulting signals to the trend component completes the whole noise reduction. Then, an intrinsic time scale decomposition algorithm is used to decompose the noise-reduced signal, screen out its effective components, and calculate their absolute energy and relative energy entropy. Finally, their variation trends with the cavitation coefficient are examined. The results show the variations in absolute energy and relative energy entropy with the cavitation coefficient manifest better regularity, offering an effective indicator of the developing status of hydraulic turbine cavitation.