摘要
提出了一种利用小波包分析提取水电机组的振动故障特征和基于支持向量机的水电机组振动故障诊断方法。以二值分类为基础,构建了基于支持向量机的多值分类器。先对水电机组的振动信号进行频谱分析,提取该信号在频率域的特征量,将频谱特征向量作为学习样本,通过训练,使分类器能够建立频谱特征向量和故障类型的映射关系,从而达到故障诊断的目的,并以水电机组振动多故障分类为例,进行了应用检验。结果表明,与常规方法相比,该方法简单有效、并具有很好的分类能力和良好的鲁棒性,可以满足在线故障诊断的要求,适合水电机组振动故障的诊断。该方法为水电机组故障诊断向智能化发展提供了新的途径。
A new method of vibrant fault diagnosis was proposed for hydro-turbine generating unit based on wavelet packet analysis and support vector machine（SVM）. A multi-class classifier was developed based on binary classification. Collecting the characteristics of this signal in frequency domain, and then using them as learning samples to train the constructed classifier so as to realize the mapping relationship between the fault and the spectrum characteristic, this method can be used for diagnosis of the unit faults efficiently. The simulation experiment shows that the proposed method has good classification ability and robust performances This method is suitable for vibration multi-fault online diagnosis of hydro-turbine generating unit. A new way is provided in intelligence diagnosis of vibration fault of hydro-turbine generating unit.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2006年第24期164-168,共5页
Proceedings of the CSEE
基金
国家自然科学基金项目(90410019)~~
关键词
水电机组
振动
故障诊断
小波包分析
支持向量机
hydro-turbine generating unit
vibration
fault diagnosis
wavelet packet analysis
support vector machine
