【摘要】 水轮机是转换水电能源的关键设备，产生空化现象将会影响机组的安全稳定运行。为精准识别机组空化故障，降低空化现象对水轮机的长期危害，提出一种分步式水轮机空化状态诊断分析方法。先通过改进小波阈值方法对水轮机运行声信号进行降噪处理，再以自编码器提取故障信号的多维特征，最后通过支持向量机完成故障识别任务。在实际运行数据上的实验故障识别精确度达99.8%，且误差为0，证明了该方法的有效性。与t-SNE、ProbPCA、AE等方法的对比实验表明，所提方法具有较高的诊断精度，较短的诊断时间，较低的误差，证明了该方法的优越性。该方法能有效剔除水轮机运行中的噪声信号，实现机组空化故障精准识别，为水轮机空化故障诊断提供了一种有效的解决方案，在工程实际应用中具有很强的推广性。更多还原

【Abstract】 Hydraulic turbine is the key equipment for converting hydropower energy while the cavitation phenomenon will affect the safe and stable operation of the unit. Therefore, in order to accurately identify the cavitation faults and reduce the long-term harm of cavitation to the turbine, this work proposes a step-by-step hydraulic turbine cavitation fault diagnosis method based on PVMD-AESVM. Firstly, the noise reduction of the hydraulic turbine operation sound signal is carried out by an improved wavelet thresholding method. Then the multi-dimensional features of the fault signal are extracted using an autoencoder, and finally, fault identification tasks are accomplished utilizing a support vector machine. The experimental fault recognition accuracy on real operating data is99.8%, with an error of 0, which proves the effectiveness of this method. Compared with t-SNE, ProbPCA and AE, the approach herein has the higher diagnostic accuracy, shorter diagnostic time and lower error, which proves its superiority. This method can effectively eliminate the noise signal during the operation of the turbine and realize accurate identification of cavitation fault in the unit, providing an effective solution for the fault diagnosis of turbine cavitation, and demonstrating strong popularization in practical engineering.更多还原