基于HHT的水轮机空化信号研究  

Investigation on turbine cavitation signals analysis based on Hilbert-Huang transform

作　　者：薛延刚[1] 王瀚[2] 

机构地区：[1]兰州工业学院,兰州730050 [2]西安理工大学,西安710048

出　　处：《水力发电学报》2015年第5期147-151,共5页Journal of Hydroelectric Engineering

摘　　要：水轮机是水力发电机组中的关键设备,空化又是水轮机组运行过程中影响其稳定性和效率的因素之一。由于水轮机结构和运行的特殊性,空化不易被直接观测,采用水轮机空化声信号监测是研究空化的有效途径。传统的傅里叶变换和目前常用的小波变换对于窄带低频信号的分析效果明显,但两种方法很难涵盖水轮机空化宽带高频信号。本文正是在此情况下,提出了一种新的空化信号分析方法,Hilbert-Huang变换(HHT)。该方法对信号具有自适应功能,经验模态分解分解能提取具有明确物理意义的水轮机空化模式分量信号。通过对同一空化信号分别进行小波和HHT分析比较,发现HHT方法更具计算准确、精度高等优点。将基于Hilbert-Huang变换方法引入到水轮发电机组空化信号特征提取中,对水轮发电机组故障诊断系统的准确度将是一个有效的提升。Cavitation of water turbine units is one of the factors influencing the stability and efficiency of their operation. Cavitation phenomena that occur in a running turbine unit are not easy to observe directly and acoustic signal monitoring is an effective way of the cavitation study. Traditional Fourier transform and wavelet transform are two commonly-used methods for analysis of narrow-banded low-frequency signals, but they are difficult to cover high-frequency components of water turbine cavitation broadband signals. This paper describes a new self-adaptive Hilbert-Huang transform （HHT） method that uses EMD decomposition to extract the physically meaningful signal components of turbine cavitation mode, and explores cavitation identification using HHT. Experimental tests show that this method is fast in diagnosis and better in generalization performances. Thus, it is a suitable method for cavitation fault diagnosis of hydroelectric unit.

关 键 词：水轮机 空化 希尔伯特-黄变换 经验模态分解 本征模式分量函数 

分 类 号：TK312[动力工程及工程热物理—热能工程]