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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。采用对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。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。
The least square support vector machine and information fusion technology are used to diagnose the vibration faults of hydropower units. The training samples are used to make the least square support vector machine reflect the mapping relationship between the eigenvector and the type of fault by using the frequency domain feature and the time domain amplitude feature of vibration signal of hydropower unit as the eigenvectors, Decision-making information fusion, so as to achieve the purpose of fault diagnosis. Taking hydroelectric generating set vibration fault diagnosis as an example, the application test is carried out. The results show that, compared with the conventional method, the least square support vector machine and the information fusion technology have the advantages of fast and effective, which is fit for the diagnosis of hydroelectric generating set vibration fault.