电工技术学报2017，Vol.32Issue(17)：156-163,8.DOI:10.19595/j.cnki.1000-6753.tces.161746
基于深度自编码网络模型的风电机组齿轮箱故障检测
Fault Detection of Wind Turbine Gearbox Based on Deep Autoencoder Network
摘要
Abstract
In order to achieve the fault detection of wind turbine gearbox,a deep autoencoder network model from deep learning method based on supervisory control and data acquisition (SCADA) data and vibration signals of wind turbine gearbox is proposed in this paper.The deep autoencoder network,as one of the typical deep learning methods,can obtain the underlying rules and distribution characteristics of the data through learning features of original sample by layer-wise intelligent learning to form a more abstract and high-level representation.Firstly,restricted boltzmann machine was used to pre-train parameters and the back-propagation algorithm was used to optimize these parameters to build the deep autoencoder model in this paper.Then through encoding and decoding condition variables of gearbox,reconstruction error was computed as the gearbox condition monitoring variable.In order to monitor the trend change of reconstruction error effectively,the adaptive threshold was chosen as the decision criterion of gearbox fault.Finally,by utilizing the record data before and after fault to simulation,results showed the validity of deep autoencoder model on gearbox fault detection.关键词
风电机组/齿轮箱/故障检测/深度自编码网络/自适应阈值Key words
Wind turbine/gearbox/fault detection/deep autoencoder/adaptive threshold分类
信息技术与安全科学引用本文复制引用
刘辉海,赵星宇,赵洪山,宋鹏,邓春..基于深度自编码网络模型的风电机组齿轮箱故障检测[J].电工技术学报,2017,32(17):156-163,8.基金项目
国家科技支撑计划项目资助(2015BAA06B03). （2015BAA06B03）
