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A Systems Engineering Approach to Incorporating the Internet of Things to Reliability-Risk Modeling for Ranking Conceptual Designs

[+] Author Affiliations
Anthony D’Angelo, Edwin K. P. Chong

Colorado State University, Ft. Collins, CO

Paper No. IMECE2018-86711, pp. V013T05A027; 10 pages
doi:10.1115/IMECE2018-86711
From:
  • ASME 2018 International Mechanical Engineering Congress and Exposition
  • Volume 13: Design, Reliability, Safety, and Risk
  • Pittsburgh, Pennsylvania, USA, November 9–15, 2018
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5218-7
  • Copyright © 2018 by ASME

abstract

This paper establishes the baseline for incorporating the Internet of Things (IoT) into the Reliability-Risk model. The authors developed the original Reliability-Risk model as a “trade-off” tool for ranking conceptual designs as a function of reliability. We summarize the original Reliability-Risk model and algorithm and discuss the process of updating the standard Integration Definition Function Modeling (IDEF0) technique with the IoT. Inserting the updated IDEF0 into the Reliability-Risk modeling framework creates a dynamic closed-loop system. We identified a concept for using a probabilistic workflow to automate the new closed-loop system and discuss a Reliability-Risk sensitivity approach.

The Reliability-Risk model ranked five conceptual packaging designs against 17 criteria for incorporation into the supply chain. The authors use a Multi-Criteria-Decision System (MCDS) to establish the rankings. The paper re-visits the original example to include data (the IoT) such as shock, temperature, and humidity obtained from various nodes in the logistics cycle. After the sensor data are incorporated, updated systems specification and reliability models resulted in a new ranking. We will discuss the results of the rankings.

Current research in developing the Digital Twin and Digital Thread are lacking in the area of logistics modeling. The incorporation of Discrete Event Simulation models to simulate transportation, handling, and storage shows promise to address these shortcomings. Therefore, we will briefly discuss our approach on incorporating Discrete Event Simulation modeling into the Reliability-Risk-IoT model to create a “logistics twin.”

Copyright © 2018 by ASME

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