Correlated representation learning has found wide usage in process monitoring. However, slow and normal changes frequently occur in practical production processes, which may lead to model mismatch and degrade monitoring performance. Therefore, updating the monitoring model online and involving recently processed data information are important. This study proposes a recursive correlated representation learning (RCRL) incorporating an approach for online model updating for adaptive monitoring of slowly varying processes. First, an initial canonical correlation analysis-based monitoring model is established using historical process data. Second, an online model updating criterion is developed, and updating procedures are provided to reflect online data information and update monitoring model in a timely manner. Then, monitoring statistics are established and decision making logic is established to identify process status. The fitness of the monitoring scheme is increased because the online process information is considered to update the model. The proposed RCRL-based monitoring scheme is applied on a numerical example and a lab-scale distillation process. The effectiveness and superiority of the RCRL approach are verified.

相关表示学习已在过程监控中得到广泛使用。但是，在实际生产过程中经常会发生缓慢而正常的变化，这可能导致模型不匹配并降低监视性能。因此，在线更新监视模型并包含最近处理的数据信息非常重要。这项研究提出了一种递归相关表示学习（RCRL），该方法结合了一种在线模型更新的方法，用于对缓慢变化的过程进行自适应监视。首先，使用历史过程数据建立初始的基于规范相关性分析的监视模型。其次，制定了在线模型更新标准，并提供了更新程序以反映在线数据信息并及时更新监视模型。然后，建立监视统计数据，并建立决策逻辑以识别过程状态。由于考虑了在线过程信息来更新模型，因此提高了监视方案的适用性。所提出的基于RCRL的监测方案应用于数值示例和实验室规模的蒸馏过程。验证了RCRL方法的有效性和优越性。