基于CNN-集成学习的多风电机组故障诊断  

Fault Diagnosis of Multi Wind Turbine Based on CNN-Ensemble Learning

作　　者：叶祎旎 李艳婷[1] YE Yini;LI Yanting(School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200241,China)

机构地区：[1]上海交通大学机械与动力工程学院,上海200241

出　　处：《工业工程》2022年第1期136-143,共8页Industrial Engineering Journal

基　　金：国家自然科学基金资助项目(71672109,71531010)。

摘　　要：海上风电场地处偏远环境,长期受到盐碱腐蚀。为解决风电机组运行过程中产生的多种故障检测识别问题,在传统卷积神经网络LeNet-5的基础上构建模型。该模型采用ReLU函数作为激活函数,增加了卷积层、池化层和全连接层。针对风电机组的监督控制和数据采集(supervisory control and data acquisition,SCADA)系统及状态监控(condition monitoring,CM)系统所提供的数据集,进行多元类别故障诊断。并对多台风电机组进行聚类分析,应用集成学习方法,构建多风电机组故障诊断模型。实验表明,所提方法取得了97%~99%的诊断精度。通过将实验结果与其他算法进行对比,验证了该方法的有效性。Offshore wind farms are located in remote environment and have been constantly corroded by saline alkali.In order to solve the problems of multiple-fault detection and identification in the operation process of wind turbines,a model is established based on the traditional convolution neural network LeNet-5.The model adopts the ReLU function as the activation function,and a convolutional layer,a pooling layer and a full connection layer are incepted.Aiming at the datasets of the wind turbine supervisory control and data acquisition(SCADA)system and the condition monitoring(CM)system,a multi-category fault diagnosis is carried out.A cluster analysis is implemented on several wind turbines,followed by ensemble learning to build a multi-machine wind turbine fault diagnosis model.The experimental results indicate that the diagnostic accuracy of the proposed method is 97%~99%.By comparing the experimental results and other algorithms,the effectiveness of the proposed method is verified.

关 键 词：故障诊断 LeNet-5网络 监督控制和数据采集 多元类别 集成学习 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论] TK83[自动化与计算机技术—计算机科学与技术]