Spatial-temporal associations representation and application for process monitoring using graph convolution neural network

by   Hao Ren, et al.

Industrial process data reflects the dynamic changes of operation conditions, which mainly refer to the irregular changes in the dynamic associations between different variables in different time. And this related associations knowledge for process monitoring is often implicit in these dynamic monitoring data which always have richer operation condition information and have not been paid enough attention in current research. To this end, a new process monitoring method based on spatial-based graph convolution neural network (SGCN) is proposed to describe the characteristics of the dynamic associations which can be used to represent the operation status over time. Spatia-temporal graphs are firstly defined, which can be used to represent the characteristics of node attributes (dynamic edge features) dynamically changing with time. Then, the associations between monitoring variables at a certain time can be considered as the node attributes to define a snapshot of the static graph network at the certain time. Finally, the snapshot containing graph structure and node attributes is used as model inputs which are processed to implement graph classification by spatial-based convolution graph neural network with aggregate and readout steps. The feasibility and applicability of this proposed method are demonstrated by our experimental results of benchmark and practical case application.


Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecasting

Accurate traffic forecasting is essential for smart cities to achieve tr...

TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification

Multivariate time series classification (MTSC) is an important data mini...

Dynamic Graph Neural Network with Adaptive Edge Attributes for Air Quality Predictions

Air quality prediction is a typical spatio-temporal modeling problem, wh...

Distributed dynamic modeling and monitoring for large-scale industrial processes under closed-loop control

For large-scale industrial processes under closed-loop control, process ...

Hybrid variable monitoring: An unsupervised process monitoring framework

Traditional process monitoring methods, such as PCA, PLS, ICA, MD et al....

Analysis of different temporal graph neural network configurations on dynamic graphs

In recent years, there has been an increasing interest in the use of gra...

Discovering Gateway Ports in Maritime Using Temporal Graph Neural Network Port Classification

Vessel navigation is influenced by various factors, such as dynamic envi...

Please sign up or login with your details

Forgot password? Click here to reset