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。该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,表明该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。
In order to improve the predictive ability of auxiliary engine faults,combined with the advantages of unsupervised learning methods in deep learning,an early fault warning method for power plant auxiliary engine based on improved stacked autoencoder network is proposed.The method takes the historical normal data of the auxiliary engine as the training set,utilizes the nonlinear expression ability of the stacked autoencoder(SAE)network to express the relationship between the variables of the auxiliary engine,and introduces the batch normalization(BN)algorithm to optimize network performance.For the input observation vectors,the SAE network gives the corresponding reconstruction vectors,constructs the similarity based on the fusion distance to represent the deviation between the observation vector and reconstruction vector.When the auxiliary engine starts to deviate from the normal state,the deviation between the observed value and reconstructed value increases,and the similarity drops to the warning threshold,which indicates that the engine fault appears.The normal data and fault data of the medium speed coal mill of a certain thermoelectric unit are used to conduct test and verification respectively.The results show that the SAE network with BN algorithm introduced has lower reconstruction error.The proposed fault warning method can make early warning before the coal mill is tripped,which indicates that the method can effectively make fault warning of auxiliary engine fault and 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,Beijing 102206,China;State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,Beijing 102206,China;State Grid Liaoning Electric Power Co.,Ltd.,Shenyang 110006,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2019年第6期39-47,共9页
Chinese Journal of Scientific Instrument
基金
国家重点研发计划(2017YFB0902100)项目资助
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 engine of power plant
early fault warning
