基于ICEEMDAN和CNN的水电机组故障诊断

Fault Diagnosis of Hydroelectric Units Based on ICEEMDAN and CNN

为实现水电机组故障的准确识别,首先结合改进的自适应白噪声完全集合经验模态分解模型(ICEEMDAN)和相关性分析方法对振动信号进行分解及重构处理;接着,为解决因卷积神经网络(CNN)输入具有多样性问题,比较分析了3种不同输入信号构造方式对CNN模型故障诊断识别准确率的影响;最终选定了一维重构信号的方形矩阵形式作为CNN的输入方式,通过某电厂转轮室碰摩故障实测数据验证该方法能有效提高水电机组故障诊断精度。

To achieve accurate identification of faults in hydroelectric units,this study first combines the improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and correlation analysis methods to decompose and reconstruct the vibration signals.Then,considering the diversity of inputs for convolutional neural networks(CNN),three different ways of constructing input signals are compared and analyzed for the accuracy of fault diagnosis and recognition by the CNN model.Finally,a square matrix representation of one-dimensional reconstructed signals is selected as the input format for CNN.The effectiveness of this method in improving the accuracy of fault diagnosis in hydroelectric units is validated through real measured data of turbine rubbing faults in a certain power plant.