Graph-driven production process monitoring

A system for production process monitoring using graph learning, comprising:

• a processor;
• and
• a memory having modules stored thereon for execution by the processor, the modules comprising an ontology engine, a graph engine, a human machine interface, and an analytics engine, wherein:
• the ontology engine is configured to generate a production ontology based on data received from an engineering design process, wherein the ontology includes selection of production machines, definition of work products and workstations, and workflow design;
• the graph engine is configured to:
• instantiate a production graph based on the production ontology, the production graph comprising nodes and edges, wherein the nodes represent components of the production ontology, including production machines and work products and the edges represent relationships between the components;
• read production process data from control systems of the production environment;
• and
• populate the production graph with production process data in real time to generate a time series of production graphs;
• the human machine interface is configured to display the time series of production graphs to reflect updated status of the production process including location of work products among the workstations;
• the analytics engine is configured to:
• execute machine learning algorithms to extract trends from the time series of production graphs;
• generate one or more predictions of the production process based on the extracted trends;
• indicate in the production graph the one or more predictions;
• receive prediction information from historical production graphs of related production processes;
• and
• perform offline runtime analytics on the production graph to yield analytics results including a plurality of predictors, wherein the predictors include knowledge from the received prediction information leveraged with a weight sharing initialization.