燕山大学学报2024，Vol.48Issue(6)：550-560,11.DOI:10.3969/j.issn.1007-791X.2024.06.009
基于优化卷积神经网络的化工过程故障诊断方法
Fault diagnosis method based on optimized convolutional neural network for chemical process
摘要
Abstract
In order to extract effective fault features from the massive monitoring data of complex chemical process,a fault diagnosis method for chemical process based on optimized Convolutional Neural Network(CNN)is proposed to find faults in time and accurately identify the fault cause.Firstly,a one-dimensional CNN for binary classification is constructed to improve the efficiency of condition monitoring.Secondly,to address the issue that CNN cannot evaluate the importance of network feature data,the attention mechanism is introduced into the fault diagnosis model of CNN,which can effectively capture feature details and suppress interference information.Different weights are assigned to the importance of the network feature data to realize the automatic extraction of key fault features for complex chemical systems.Then,to tackle the low efficiency challenge in manual hyperparameter optimization,the Tree-structured Parzen Estimator(TPE)hyperparameter optimization technology is used to realize the precise tuning of hyperparameter combination with flexible modeling method and efficient acquisition function.The optimized fault diagnosis model of CNN is constructed.Finally,the Tennessee Eastman(TE)process is used to verify the effectiveness of the proposed method.The results show that the proposed method can detect multiple failure modes in a timely and effective manner,which can provide a reliable decision-making basis for maintenance staff.关键词
卷积神经网络/化工过程/故障诊断/注意力机制/超参数优化Key words
convolutional neural network/chemical process/fault diagnosis/attention mechanism/hyperparameter optimization分类
资源环境引用本文复制引用
胡宇鹏,郭丽杰,张子龙,康建新,崔超宇,乔桂英..基于优化卷积神经网络的化工过程故障诊断方法[J].燕山大学学报,2024,48(6):550-560,11.基金项目
河北省自然科学基金资助项目(E2021203069) （E2021203069）
秦皇岛市科技计划项目(202101A319) （202101A319）
