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

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

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
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 auto-matic 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 power-ful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is per-formed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,EfficientNet-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The fea-sibility and validity of this framework are verified utilizing exten-sive numerical and industrial cases.Compared with state-of-the-art oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstation-arity.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.

Convolutional neural networks(CNNs)deep learn-ingimage processingoscillation detectionprocess industries

Tao Wang、Qiming Chen、Xun Lang、Lei Xie、Peng Li、Hongye Su

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Department of Electronic Engineering, the School of Information, Yunnan University, Kunming650091, China

DAMO Academy, Alibaba Group, Hangzhou 311100, China

State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China

国家自然科学基金国家自然科学基金Major Project of Science and Technology of Yunnan ProvinceMajor Project of Science and Technology of Yunnan ProvinceYunnan University Professional Degree Graduate Practice Innovation Fund Project

6200329862163036202202AD080005202202 AH080009ZC-22222770

2024

10.1109/JAS.2023.124170

自动化学报(英文版)
中国自动化学会,中国科学院自动化研究所,中国科技出版传媒股份有限公司

自动化学报(英文版)

CSTPCDEI
ISSN：2329-9266
年,卷(期)：2024.11(4)
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