Detection of Oscillations in Process Control Loops From Visual Image Space Using Deep Convolutional Networks  

作　　者：Tao Wang Qiming Chen Xun Lang Lei Xie Peng Li Hongye Su 

机构地区：[1]the Department of Electronic Engineering,the School of Information,Yunnan University,Kunming 650091,China [2]DAMO Academy,Alibaba Group,Hangzhou 311100,China [3]the State Key Laboratory of Industrial Control Technology,Institute of Cyber-Systems and Control,Zhejiang University,Hangzhou 310027,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第4期982-995,共14页自动化学报（英文版）

基　　金：the National Natural Science Foundation of China(62003298,62163036);the Major Project of Science and Technology of Yunnan Province(202202AD080005,202202AH080009);the Yunnan University Professional Degree Graduate Practice Innovation Fund Project(ZC-22222770)。

摘　　要：Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.

关 键 词：Convolutional neural networks(CNNs) deep learning image processing oscillation detection process industries 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]