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Generating Digital Twin models using Knowledge Graphs for Industrial Production Lines

Authors: Agniva Banerjee, Raka Dalal, Sudip Mittal, and Karuna Pande Joshi

Book Title: Workshop on Industrial Knowledge Graphs, co-located with the 9th International ACM Web Science Conference 2017

Date: June 25, 2017

Abstract: Digital Twin models are computerized clones of physical assets that can be used for in-depth analysis. Industrial production lines tend to have multiple sensors to generate near real-time status information for production. Industrial Internet of Things datasets are difficult to analyze and infer valuable insights such as points of failure, estimated overhead. etc. In this paper we introduce a simple way of formalizing knowledge as digital twin models coming from sensors in industrial production lines. We present a way on to extract and infer knowledge from large scale production line data, and enhance manufacturing process management with reasoning capabilities, by introducing a semantic query mechanism. Our system primarily utilizes a graph-based query language equivalent to conjunctive queries and has been enriched with inference rules.

Type: InProceedings

Address: Troy, NY, USA

Tags: digital twin, knowledge graph, big data, industrial internet of things, semantic web

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