水利学报2017，Vol.48Issue(3)：334-340,350,8.DOI:10.13243/j.cnki.slxb.20160918
基于随机共振和经验模态分解的水力发电机组振动故障诊断
Vibration fault diagnosis of hydroelectric generating unit by using stochastic resonance and Empirical Mode Decomposition
摘要
Abstract
Aiming at the low accuracy problems caused by the difficulty of weak signals detection in fault diagnosis for actual hydroelectric generating unit,this paper presents a weak signal detection method based on stochastic resonance (SR) and Empirical Mode Decomposition (EMD).This method first reduces noise signal of a vibration signal using stochastic resonance to enhance its stochastic resonance,then uses EMD to decompose its output signal and energy method to extract its feature vectors.Taking the feature vectors as input,a genetic algorithm optimization and support vector machine model is able to achieve identification and diagnosis of the signal faults.The simulation results show that this method can accurately identify the unit's abnormal situation with high accuracy in fault diagnosis.关键词
随机共振/EMD/支持向量机/故障诊断/水力发电机组Key words
stochastic resonance/EMD/support vector machines/fault diagnosis/hydroelectric generating unit分类
信息技术与安全科学引用本文复制引用
贾嵘,李涛涛,夏洲,马喜平..基于随机共振和经验模态分解的水力发电机组振动故障诊断[J].水利学报,2017,48(3):334-340,350,8.基金项目
国家自然科学基金项目(51279161) （51279161）
陕西水利科技计划项目(2015slkj-04) （2015slkj-04）
