用于控制阀静摩擦检测的最优传输图像表示与深度协方差对齐（CORAL）
Optimal Transport Image Representation and Deep Covariance Alignment (CORAL) for Control Valve Stiction Detection
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中文总结 AI 辅助
针对工业过程中控制阀静摩擦检测问题，结合最优传输成像技术与深度相关对齐算法，提出新方法。通过将闭环信号转换为OT图像，利用CNN编码器学习领域不变表示，在测试集上取得良好效果，有效减轻领域偏移，为实际工业控制回路提供可靠的静摩擦检测。
中文摘要 AI 辅助
控制阀静摩擦是工业过程中不必要振荡和控制回路性能不佳的常见原因。数据驱动方法可自动检测静摩擦，但纯基于模拟数据训练的模型常因领域偏移难以推广到实际工业控制回路。为此，本文提出一种结合最优传输（OT）成像技术与深度相关对齐（Deep CORAL）算法的静摩擦检测新方法。将闭环信号转换为二维OT图像以捕捉控制回路动态行为。该方法包括一个卷积神经网络（CNN）编码器，通过优化组合目标学习领域不变表示，在20个基准回路的独立测试集上评估下游分类器，结果显示该方法成功诊断出20个回路中的18个，在所有13个静摩擦案例中召回率达100%，准确率90.00%，F1分数92.86%，显著减轻领域偏移，为实际工业控制回路提供可靠的静摩擦检测。
英文摘要
Control valve stiction is a common cause of unwanted oscillations and poor control-loop performance in industrial processes. Data-driven methods can automatically detect stiction, but models trained purely on simulated data often struggle to generalize to real industrial control loops due to domain shift. To bridge this gap, this work propose a novel stiction detection methodology that combines optimal transport (OT) imaging technique with deep correlation alignment (Deep CORAL) algorithm. Closed loop signals: controller output and process variable are converted into two-dimensional OT images. These images capture the dynamic behaviour of control loops. The proposed methodology includes a convolutional neural network (CNN) encoder (or feature extractor) trained to learn domain-invariant representations by optimizing a combined objective: a cross-entropy loss on labeled simulation data and a Deep CORAL (covariance-alignment) loss between simulation data and unlabeled target-domain industrial data. Downstream classifiers trained on the domain-invariant target features were evaluated on an independent test set of 20 benchmark loops from industrial stiction data benchmark. The proposed methodology successfully diagnosed 18 out of the 20 loops and achieved100% recall across all 13 stiction cases, an accuracy of 90.00% and an F1-score of 92.86%. Compared to standard baseline approach (hand-crafted features-based method), the proposed methodology significantly mitigates domain shift, providing robust, highly reliable stiction detection for real-world industrial control loops.
