Hybrid-Order Graph Embedded Distributed Encoder-Decoder for Multiunit Industrial Plant-Wide Process Monitoring

In multiunit industrial plant-wide processes, how to simultaneously model the dynamic correlations within units and the graph-structured interactions between units has not been explored by existing distributed monitoring methods. To address this issue, this work proposes a novel hybrid-order graph embedded distributed encoder-decoder (HGDED) for plant-wide process monitoring. Firstly, the whole process is decomposed into multiple operation units and characterized as a directed graph based on process knowledge. Then, distributed node encoder and decoder are developed under the sequence-to-sequence framework to capture the intra-unit temporal dependence. Between the encoder and decoder, a novel hybrid-order graph convolutional network (HGCN) is designed to simultaneously embed the inter-unit spatial dependence. Through multireceptive field graph convolution and spatial recurrent updates, HGCN can effectively exploit hybrid-order neighbor information to capture multi-cascaded interactions between units. Moreover, a spatial order optimizer is innovatively proposed to automatically learn the optimal discrete orders for different nodes, which helps HGCN better capture significant features from higher-order neighbor nodes. With concurrent analysis of temporal and spatial dependencies, the representation learning ability of HGDED is enhanced, thereby improving the monitoring performance. Finally, the effectiveness of the proposed method is demonstrated through the Tennessee Eastman process and a real-world air separation process.
Note to Practitioners
—The complex process characteristics and topology structure have imposed considerable challenges on monitoring multiunit industrial plant-wide processes. In practice, it is necessary to extract both the intra-unit temporal dependence and the inter-unit spatial dependence for distributed monitoring. Existing studies fail to consider these two important characteristics at the same time, which may lead to information loss and performance degradation. In this work, the proposed HGDED not only develops distributed node encoder and decoder to model the dynamic correlations within units, but also incorporates a HGCN to capture the interactions between units. In particular, HGDED explicitly models the whole process into a directed graph, which can characterize structural relationships in a more reasonable and fine-grained way. Considering the multi-cascaded information transmission between units, the HGCN is designed to exploit hybrid-order neighbor information for graph embedding, in which the optimal discrete orders are automatically determined by the spatial order optimizer. As a consequence, HGDED can better model process behaviors and extract more representative features to improve monitoring performance. In addition, HGDED follows an end-to-end training fashion, which is convenient for engineers to implement. The proposed method has been validated on two industrial cases, and it is suitable for monitoring various practical industrial plant-wide processes.