基于经验模态分解和支持向量机的水电机组振动故障诊断

水电机组的振动信号为典型的非平稳、非线性信号。为了通过振动信号正确判断水电机组的运行状态,本文提出运用经验模态分解处理原始信号,并对获得的基本模式分量计算其复杂度特征,最后运用最小二乘支持向量机进行故障诊断。选取径向基函数作为核函数,并通过网格搜索和交叉验证确定相关参数。结果表明,经验模态分解复杂度特征和支持向量机结合,能够准确地实现故障诊断,确定故障类型,为机组运行维护人员提供参考依据。

Fault diagnosis of vibration for hydropower units based on empirical mode decomposition and support vector machine

Vibration signals of hydropower units are typically non-linear and non-stationary. To diagnose and analyze such signals, this paper presents an original signal processing method of applying empirical mode decomposition (EMD) and demonstrates the then calculation procedure of intrinsic mode functions and their complexity features. Fault diagnosis of the signals was carried out using the least squares support vector machine (LS-SVM), and by taking the radial basis function as the kernel function, its relevant parameters were determined through grid search and cross validation. The results show that coupling EMD decomposition with SVM in analysis of complexity features provides a rather accurate device for fault diagnosis and determination of fault type, thus laying a basis for operation and maintenance of hydropower units.