摘要
针对水电机组轴系振动故障诊断,提出多重分形谱和支持向量机相结合的故障分类识别方法。该方法首先利用多重分形谱算法提取振动信号的特征数据,然后将该特征数据作为支持向量机的输入向量来实现故障的分类识别。实验数据表明,该方法能比较准确地识别轴系常见故障,为水电机组轴系故障智能识别提供了一种新的思路。
A method based on multifractal spectrum and support vector machine is proposed for fault identification of shafting system of hydropower units.Firstly,the multifractal spectrum algorithm is used to extract the feature data of vibration signals,and then the feature data is used as the input vector of support vector machine to realize the fault classification.The experimental data show that the method can accurately identify the common shafting faults,and provide a new method for the intelligent identification of shafting faults of hydropower units.
作者
孟繁聪
吉俊杰
薛小兵
姚航宇
胡飞
潘伟峰
白亮
MENG Fancong;JI Junjie;XUE Xiaobing;YAO Hangyu;HU Fei;PAN Weifeng;BAI Liang(East China Yixing Pumped Storage Power Co.Ltd.,Yixing 214205,China;NARI Group Corporation(State Grid Electric Power Research Institute),Nanjing 211000,China;Xi′an University of Technology,Xi′an 710048,China)
出处
《湖南电力》
2022年第5期114-118,共5页
Hunan Electric Power
基金
国网新源控股有限公司科技项目(SGXYKY-2020-031)。
关键词
水电机组
多重分形
支持向量机
故障识别
hydropower unit
multifractal
support vector machine
fault identification
