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, EfficientNet-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-the-art 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.

中文翻译：

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使用深度卷积网络从视觉图像空间检测过程控制回路中的振荡

由于振荡环路的高发生率及其对工厂盈利能力的负面影响，振荡检测一直是行业中的热门研究课题。尽管已经提出了许多自动检测技术，但大多数只能解决部分实际困难。振荡被启发性地定义为视觉上明显的周期性变化。然而，手动目视检查是劳动密集型的并且容易漏检。受动物视觉系统的启发，卷积神经网络（CNN）具有强大的特征提取能力。在这项工作中，对用于视觉振荡检测的典型 CNN 模型进行了探索。具体来说，我们测试了MobileNet-V1、ShuffleNet-V2、EfficientNet-B0和GhostNet模型，发现这样的视觉框架非常适合振荡检测。利用大量的数值和工业案例验证了该框架的可行性和有效性。与最先进的振荡检测器相比，建议的框架更简单，对噪声和平均非平稳性更鲁棒。此外，该框架具有良好的泛化能力，能够处理训练数据中不存在的特征，例如多重振荡和异常值。