基于CNN-集成学习的多风电机组故障诊断

doi:10.3969/j.issn.1007-7375.2022.01.017

工业工程 ›› 2022, Vol. 25 ›› Issue (1): 136-143.doi: 10.3969/j.issn.1007-7375.2022.01.017

基于CNN-集成学习的多风电机组故障诊断

叶祎旎, 李艳婷

上海交通大学机械与动力工程学院, 上海 200241

收稿日期:2020-07-06 发布日期:2022-03-02
作者简介:叶祎旎(1995—)，女，重庆市人，硕士研究生，主要研究方向为数据驱动的故障诊断
基金资助:
国家自然科学基金资助项目(71672109, 71531010)

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

YE Yini, LI Yanting

School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200241, China

Received:2020-07-06 Published:2022-03-02

摘要/Abstract

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。

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

Abstract: 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.

Key words: fault diagnosis, LeNet-5 network, supervisory control and data acquisition (SCADA), multi-category, ensemble learning

中图分类号:

TP312
TK83

叶祎旎, 李艳婷. 基于CNN-集成学习的多风电机组故障诊断[J]. 工业工程, 2022, 25(1): 136-143.

YE Yini, LI Yanting. Fault Diagnosis of Multi Wind Turbine Based on CNN-Ensemble Learning[J]. Industrial Engineering Journal, 2022, 25(1): 136-143.

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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

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