基于最小二乘支持向量机和信息融合技术的水电机组振动故障诊断(英文)

Vibration Fault Diagnosis of Hydroelectric Unit Based on LS-SVM and Information Fusion Technology

应用最小二乘支持向量机和信息融合技术对水电机组的振动故障进行诊断。采用以水电机组振动信号的频域特征和时域振幅特征作为特征向量的学习样本,通过训练,使最小二乘支持向量机能够反映特征向量和故障类型的映射关系,在完成局部诊断后再实现决策信息融合,从而达到故障诊断的目的。以水电机组振动故障诊断为例,进行了应用检验。研究结果表明,与常规方法相比,最小二乘支持向量机和信息融合技术相结合的方法具有快速有效等优点,适合水电机组振动故障的诊断。

Vibration fault diagnosis of hydroelectric unit was investigated using method of least square support vector machine （LS-SVM） and Dempster-Shafer theory （D-S Theory）. Spectrum and amplitude characteristic was acted as eigenvector of learning samples to train the constructed LS-SVM regression and classifier for realizing mapping relationship between the fault and the characteristic. Information fusion was realized after completing local diagnosis, and then fault diagnosis was achieved. Experiments show that the method has a rapidly diagnostic process and generalization performances. It is suitable for the vibration fault diagnosis of hydroelectric unit.