基于SVD-CWT和CNN的水轮发电机转子故障识别

Rotor Fault Identification of Hydro-generator Based on SVD-CWT and CNN

水轮发电机转子振动故障识别是水电站运维的重难点问题,为此提出一种基于转子振动信号的故障识别方法。首先针对发电机转子的非平稳和非线性振动信号,采用奇异值分解(SVD)并结合能量差分谱理论进行降噪预处理;对预处理数据使用连续小波变换(CWT)转换为时频图并形成图像数据集;然后将该图像数据集作为卷积神经网络(CNN)输入,通过CNN多层池化及卷积形成分布式故障特征表达,最终实现发电机转子故障模式识别和分类。经实验验证,该方法准确率达到99.5%以上,能有效识别出发电机转子的故障类型。

The rotor vibration fault identification of hydro-generator is a difficult problem in hydropower station operation and maintenance.Therefore,a fault identification method based on rotor vibration signal is proposed.Firstly,for the non-stationary and non-linear vibration signals of generator rotor,singular value decomposition(SVD)is used for denoising preprocessing combined with energy difference spectrum theory;the preprocessed data is transformed into time-frequency graph by continuous wavelet transform(CWT)and formed into image data set.Then the image data set is used as convolutional neural network(CNN)input,and distributed fault feature expression is formed by CNN multi-layer pooling and convolution.Finally hydro-generator rotor fault mode recognition and classification are realized.The experimental results show that the accuracy of this method is above 99.5%,which can effectively identify the fault types of hydro-generator rotor.