Digital twins-based predictive models find their roots in smart manufacturing. However, their potential applications to control chart pattern recognition (CCPR) algorithms, which lie at the heart of advanced fault detection systems, remain underexplored. A key challenge in CCPR models arises from the intrinsic imbalance between classes, which can compromise the model’s performance if left untreated. Further, existing CCPR models are often trained over simulated control chart data in which abnormal patterns are generated separately from abnormal signals; the resulting classifiers, however, perform poorly in the early detection of abnormalities in real-time production environments. To address these challenges, we develop a cost-sensitive, bi-directional long short-term memory neural network for data sequences with mixed normal and abnormal signals. We further introduce a novel adaptive weighting strategy for data generation by enforcing the rates of abnormal signals within mini-batch distributions. Our model benefits from a new bi-objective early stopping technique, which optimally balances loss minimization and G-mean maximization for model training. Finally, we introduce a novel rolling window-based metric for evaluating CCPR classifier stability. We conduct a comprehensive experimental study of our model using both simulated data and two real-world datasets collected from biomanufacturing and wafer industries. The results of our study consistently demonstrate the superiority of our proposed stopping technique over traditional methods. Our experiments further show the effectiveness of our proposed model in maintaining the classifier stability and specifying optimal process monitoring window length within the datasets.

中文翻译：

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用于控制图模式早期检测的不平衡感知 BiLSTM

基于数字孪生的预测模型植根于智能制造。然而，它们在控制图模式识别 (CCPR) 算法（高级故障检测系统的核心）方面的潜在应用仍未得到充分探索。 CCPR 模型的一个关键挑战来自于类之间的内在不平衡，如果不加以处理，可能会损害模型的性能。此外，现有的 CCPR 模型通常是在模拟控制图数据上进行训练的，其中异常模式是与异常信号分开生成的；然而，由此产生的分类器在实时生产环境中早期检测异常方面表现不佳。为了应对这些挑战，我们开发了一种成本敏感的双向长短期记忆神经网络，用于具有混合正常和异常信号的数据序列。我们进一步引入了一种新颖的自适应加权策略，通过强制小批量分布内的异常信号率来生成数据。我们的模型受益于新的双目标早期停止技术，该技术可以最佳地平衡模型训练的损失最小化和 G 均值最大化。最后，我们引入了一种新颖的基于滚动窗口的指标来评估 CCPR 分类器的稳定性。我们使用模拟数据和从生物制造和晶圆行业收集的两个真实数据集对我们的模型进行了全面的实验研究。我们的研究结果一致证明了我们提出的停止技术相对于传统方法的优越性。我们的实验进一步证明了我们提出的模型在保持分类器稳定性和指定数据集中最佳过程监控窗口长度方面的有效性。