A "smart component" data model in PLM

Abstract

Physical products are becoming smarter because of their increased number of embedded sensors and their real-time information-processing capabilities. Data analytics, particularly predictive analytics, is one of the most important of these capabilities because it uses statistical or machine-learning techniques to determine causal relations between input and output parameters. Many researchers have addressed the challenges in creating and evaluating predictive models. Few, however, have discussed how to employ such models effectively throughout a product's life cycle. In this paper, we address this issue by extending Product Lifecycle Management (PLM) systems to include "Smart Component" data models that incorporate predictive models as "parts" or "services" of products in their master records in PLM. These smart-component data models can be modularized, composed, reused, traced, maintained, and replaced on demand. We describe a prototype system to demonstrate the feasibility of the proposed data models using an open-source PLM platform.

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