摘要
针对现有基于自编码器(AE)的过程监测方法在故障隔离和识别方面存在的缺陷,提出了一种基于去噪自编码器(DAE)的故障隔离与识别方法,其主要思路是通过在DAE的优化目标函数中引入未知的故障子空间实现故障的隔离和识别.考虑到故障的特性,引入了l;正则化项以实现稀疏隔离,并设计了基于自适应矩估计(ADAM)的优化问题求解方法.与传统方法相比,基于去噪自编码器的故障隔离与识别方法在非线性过程故障诊断中具有更好的效果.在Tennessee Eastman(TE)过程和高炉炼铁过程中的应用验证了所提出方法的可行性.
To overcome the defects of autoencoder-based process monitoring in fault isolation and identification,we propose a denoising autoencoder(DAE)-based fault isolation and identification method by introducing unknown fault subspace into the optimized objective function of DAE.Considering the characteristics of the faults,we present anl1 regularization term to achieve sparse isolation and design an optimization problem-solving method based on adaptive moment estimation.Compared with traditional methods,the DAE-based fault isolation and identification method are more effective in fault diagnosis of nonlinear processes.The application in the Tennessee Eastman(TE)process and blast furnace ironmaking process validate the feasibility of the proposed method.
作者
王浙超
曾九孙
谢磊
陶银罗
WANG Zhechao;ZENG Jiusun;XIE Lei;TAO Yinluo(College of Metrology and Measurement Engineering,China Jiliang University,Hangzhou 310018,China;College of Control Science and Engineering,Zhejiang University,Hangzhou 310027,China;Yuanpei College,Shaoxing University,Shaoxing 312000,China)
出处
《信息与控制》
CSCD
北大核心
2021年第6期641-650,共10页
Information and Control
基金
国家重点研发计划资助项目(2018YFF0214701)
国家自然科学基金资助项目(61673358)。
关键词
去噪自编码器
故障隔离与识别
重构
自适应矩估计
denoising autoencoder
fault isolation andidentification
reconstruction
adaptive moment estimation
作者简介
王浙超(1996-),男,硕士生,研究领域为过程监测与故障诊断;通信作者:曾九孙(1982-),男,博士,教授.研究领域为工业大数据分析,过程监测与故障诊断.jszeng@cjlu.edu.cn;谢磊(1979-),男,博士,教授.研究领域为控制系统性能评估与预测控制。
