IEEE Trans Cybern. 2022 Aug;52(8):8340-8351. doi: 10.1109/TCYB.2021.3050398. Epub 2022 Jul 19.
Modern industrial plants generally consist of multiple manufacturing units, and the local correlation within each unit can be used to effectively alleviate the effect of spurious correlation and meticulously reflect the operation status of the process system. Therefore, the local correlation, which is called spatial information here, should also be taken into consideration when developing the monitoring model. In this study, a cascaded monitoring network (MoniNet) method is proposed to develop the monitoring model with concurrent analytics of temporal and spatial information. By implementing convolutional operation to each variable, the temporal information that reveals dynamic correlation of process data and spatial information that reflects local characteristics within individual operation unit can be extracted simultaneously. For each convolutional feature, a submodel is developed and then all the submodels are integrated to generate a final monitoring model. Based on the developed model, the operation status of the newly collected sample can be identified by comparing the calculated statistics with their corresponding control limits. Similar to the convolutional neural network (CNN), the MoniNet can also expand its receptive field and capture deeper information by adding more convolutional layers. Besides, the filter selection and submodel development in MoniNet can be replaced to generalize the proposed network to many existing monitoring strategies. The performance of the proposed method is validated using two real industrial processes. The illustration results show that the proposed method can effectively detect process anomalies by concurrent analytics of temporal and spatial information.
现代工业装置通常由多个制造单元组成，每个单元内的局部相关性可用于有效缓解虚假相关性的影响，并细致反映过程系统的运行状态。因此，在开发监测模型时，也应考虑局部相关性，这里将其称为空间信息。在本研究中，提出了级联监测网络（MoniNet）方法，用于开发具有时间和空间信息协同分析的监测模型。通过对每个变量实施卷积操作，可以同时提取过程数据动态相关性的时间信息和单个操作单元内局部特征的空间信息。针对每个卷积特征，开发一个子模型，然后将所有子模型集成以生成最终的监测模型。基于所开发的模型，可以通过将计算的统计数据与相应的控制限进行比较来识别新采集样本的运行状态。与卷积神经网络（CNN）类似，MoniNet 通过添加更多的卷积层可以扩展其感受野并捕获更深层次的信息。此外，可以替换 MoniNet 中的滤波器选择和子模型开发，以将所提出的网络推广到许多现有的监测策略中。使用两个真实工业过程验证了所提出方法的性能。说明结果表明，该方法可以通过时间和空间信息的协同分析有效检测过程异常。
