摘要
针对锅炉受热面积灰将会降低传热效率和安全性,采用清洁因子作为健康指标来监测锅炉受热面健康状况,并且提出融合经验模态分解(EMD)和长短期记忆网络(LSTM)的模型来预测未来锅炉积灰。经验模态分解可以将时间序列分解为一系列频域稳定的本征模态函数,长短期记忆网络拥有记忆功能,它能够通过学习来挖掘时间序列之间隐藏的长期依赖关系,二者结合,增加了对于时间序列预测的准确度。通过仿真软件验证,该模型对锅炉受热面积灰状况的预测有较为满意的精度,并与两种常用模型进行对比发现,预测精度分别提升了67.7%与59.2%,验证了该模型的可行性与有效性。
In view of the fact that the ash in the heated surface of the boiler will reduce the heat transfer efficiency and safety,uses the cleanliness factor as a healthy indicator to monitor the health of the heated surface of the boiler,and proposes a model that combines empirical mode decomposition(EMD)and long short-term memory(LSTM)to predict future boiler ash deposit.EMD can decompose a time series into a series of intrinsic mode functions which are stable in frequency domain,both LSTM has a memory function,it can learn to mine hidden long-term dependencies between time series,the combination of the two increases the accuracy of time series prediction.It is verified by simulation software that the model has satisfactory accuracy in the prediction of the ash condition of the heated surface of the boiler,and compared with two commonly used models,it was found that the prediction accuracy increased by 67.7%and 59.2%respectively and the feasibility and validity of the model are verified.
作者
李孟威
史元浩
杨彦茹
张泽慧
刘文海
Li Mengwei;Shi Yuanhao;Yang Yanru;Zhang Zehui;Liu Wenhai(School of Electrical and Control Engineering,North University of China,Taiyuan 030000,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2020年第11期166-172,共7页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(61533013)
山西省重点研发计划项目(201703D111011)
山西省自然科学基金(201801D121159)
山西省青年自然科学基金(201801D221208)
山西省研究生教育创新项目(2020SY405,2020SY408)资助
关键词
清洁因子
锅炉积灰
经验模态分解
长短期记忆网络
cleanliness factor
boiler ash deposit
empirical mode decomposition
long short-term memory
作者简介
李孟威,2019年于山西工程技术学院获得学士学位,现为中北大学硕士研究生,主要研究方向是复杂系统的故障预测与健康管理。E-mail:1240459451@qq.com
