A novel denoising method of the hydro-turbine runner for fault signal based on WT-EEMD

被引:36
作者
Dao, Fang [1 ]
Zeng, Yun [1 ]
Qian, Jing [1 ]
机构
[1] Kunming Univ Sci & Technol, Fac Met & Energy Engn, Kunming 650031, Peoples R China
基金
中国国家自然科学基金;
关键词
Hydro-turbine; Acoustic vibration; Sediment wear; EEMD; Wavelet threshold denoising; EMPIRICAL MODE DECOMPOSITION; ABRASIVE EROSION; COMPONENT ANALYSIS; FEATURE-EXTRACTION; VIBRATION; WEAR; CLASSIFICATION; DIAGNOSIS; NOISE;
D O I
10.1016/j.measurement.2023.113306
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Sediment wear is a significant cause of hydro-turbine failure. The wavelet threshold and ensemble empirical mode decomposition (WT-EEMD) method is proposed to denoising the acoustic vibration signals of hydroturbine runners under normal and sand-laden water flow conditions. Ensemble empirical mode decomposition (EEMD) is performed on the acquired signals, and the decomposed high-frequency intrinsic mode function (IMF) is denoised using wavelet threshold. A nonlinear threshold function is constructed instead of the traditional threshold function in the wavelet threshold algorithm. The experimental results show that the WT-EEMD method is superior to the EMD, EEMD, and wavelet threshold. Moreover, it was found that when the sand-laden water flows through the hydro-turbine, it causes a change in the frequency spectrum. This study can provide a reference for the study of sand avoidance operation of hydro-turbines and provide a valuable supplement to the existing condition monitoring and fault diagnosis system of hydroelectric generators.
引用
收藏
页数:16
相关论文
共 47 条
[1]   Novel Features and PRPD Image Denoising Method for Improved Single-Source Partial Discharges Classification in On-Line Hydro-Generators [J].
Araujo, Ramon C. F. ;
de Oliveira, Rodrigo M. S. ;
Brasil, Fernando S. ;
Barros, Fabricio J. B. .
ENERGIES, 2021, 14 (11)
[2]   Power and energy measurement devices: A review, comparison, discussion, and the future of research [J].
Babuta, Aniket ;
Gupta, Bhavna ;
Kumar, Abhimanyu ;
Ganguli, Souvik .
MEASUREMENT, 2021, 172
[3]   Gear fault diagnosis based on a new wavelet adaptive threshold de-noising method [J].
Cai, Jianhua .
INDUSTRIAL LUBRICATION AND TRIBOLOGY, 2019, 71 (01) :40-47
[4]   Acoustic Vibration Approach for Detecting Faults in Hydroelectric Units: A Review [J].
Dao, Fang ;
Zeng, Yun ;
Zou, Yidong ;
Li, Xiang ;
Qian, Jing .
ENERGIES, 2021, 14 (23)
[5]   Eccentrically mounted rotor pack and its influence on the vibration and noise of an asynchronous generator [J].
Donat, Martin ;
Dusek, Daniel .
JOURNAL OF SOUND AND VIBRATION, 2015, 344 :503-516
[6]  
[杜晋 Du Jin], 2016, [表面技术, Surface Technology], V45, P154
[7]  
Faria MTC, 2014, PROCEEDINGS OF THE ASME INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, 2013, VOL 14
[8]   Vibration signal models for fault diagnosis of planet bearings [J].
Feng, Zhipeng ;
Ma, Haoqun ;
Zuo, Ming J. .
JOURNAL OF SOUND AND VIBRATION, 2016, 370 :372-393
[9]  
Finnie I., 1960, Wear, V3, P87, DOI [10.1016/0043-1648(60)90055-7, DOI 10.1016/0043-1648(60)90055-7, 10.1016/00431648(60)90055-7]
[10]   Denoising of an ultraviolet light received signal based on improved wavelet transform threshold and threshold function [J].
Guo, Hua ;
Yue, Leihui ;
Song, Peng ;
Tan, Yumei ;
Zhang, Lijian .
APPLIED OPTICS, 2021, 60 (28) :8983-8990