How to Navigate the Complicated World of Modern Industrial Establishments
summary
A method converts large STAMP safety-control diagrams into a searchable knowledge graph so long tangled diagrams become queryable assets: an ontology maps controllers, controlled processes, sensors, actuators and feedbacks, a Python image/XML-to-CSV extractor detects connection patterns, and Neo4j imports the CSV for graph analysis. The pipeline surfaces shortest control paths, top feedback senders, and predicts missing links; tested with industrial regulation documents, lab test data and a case study for a large energy company, motivated by disasters like Bhopal (≈4,000 immediate deaths, ≈500,000 exposed). STAMP-to-graph translator Neo4j knowledge graph Python image-to-CSV extractor
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chapters (8)
- 0:00Introduction to project
- 2:16Historical industrial disasters
- 6:50STAMP model and safety management
- 14:47Complexity of real-world systems
- 17:16From STAMP to Knowledge Graph
- 23:23Benefits of the ontology
- 26:21Integrating data for safety decisions
- 28:54Acknowledgments and Q&A
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