Detection of Oscillations in Process Control Loops From Visual Image Space Using Deep Convolutional NetworksOA北大核心CSTPCDEI

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.