摘要
为了提高对辅机故障的事前预知能力,结合深度学习中非监督学习方法的优势,提出基于改进堆叠自编码网络的电站辅机故障预警方法。以辅机的历史正常数据为训练集,利用堆叠自编码(SAE)网络的非线性表达能力表示辅机各变量之间的关系,同时引入批标准化(BN)算法优化网络性能。对于输入的观测向量,SAE网络给出相应的重构向量。构造基于融合距离的相似度表示观测向量与重构向量间的偏差,当辅机开始偏离正常状态时,观测值与重构值偏差增大,相似度下降至预警阈值即表明设备出现故障。分别利用某热电机组中速磨煤机的正常数据与故障数据进行测试与验证,结果显示引入BN算法的SAE网络具有更低的重构误差,同时能够在磨煤机跳闸前做出预警,表明所提方法可对辅机故障进行有效预警,具有一定的工程应用价值。
To improve the prediction ability of auxiliary equipment fault,a fault warning method for power plant auxiliary equipment is proposed.It is based on improved stacked autoencoder network,which fuses the advantages of unsupervised learning methods in deep learning.The method takes the historical normal data as the training set and utilizes the nonlinear expression ability of the stacked autoencoder(SAE)network to indicate the relationship among the variables of the auxiliary equipment.The batch normalization(BN)algorithm is utilized to optimize network performance.For the input observation vector,SAE network offers the corresponding reconstruction vector.The similarity based on the fusion distance is constructed to represent the deviation between the observation vector and the reconstruction vector.When the auxiliary equipment starts to deviate from the normal state,the difference between the observed value and the reconstructed value increase.The similarity drops to the warning threshold,which indicates that the machine is fault.Normal data and fault data of a medium speed mill of a thermoelectric unit are used to test and verify the proposed method.Experimental results show that SAE network with BN algorithm has lower reconstruction error.The model can provide fault warning before the coal mill trips.Therefore,the method can effectively make fault warning of auxiliary equipment,which has certain engineering application value.
作者
李晓彬
牛玉广
葛维春
罗桓桓
周桂平
Li Xiaobin;Niu Yuguang;Ge Weichun;Luo Huanhuan;Zhou Guiping(School of Control and Computer Engineering,North China Electric Power University,Beijing102206,China;State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,Beijing102206,China;State Grid Liaoning Electric Power Co.,Ltd.,Shenyang110006,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2019年第7期55-63,共9页
Chinese Journal of Scientific Instrument
基金
国家重点研发计划(2017YFB0902100)资助项目
关键词
堆叠自编码网络
批标准化
网络性能优化
电站辅机
故障预警
stacked autoencoder network
batch normalization
network performance optimization
auxiliary equipment of power plant
fault warning
