Deep Component-Based Neural Network Energy Modelling for Early Design Stage Prediction

Author(s):  
Sundaravelpandian Singaravel ◽  
Philipp Geyer
2021 ◽  
Vol 172 ◽  
pp. 107617
Author(s):  
Sang-Kwon Lee ◽  
Hwajin Lee ◽  
Jiseon Back ◽  
Kanghyun An ◽  
Youngsam Yoon ◽  
...  

Author(s):  
Lukman Irshad ◽  
Salman Ahmed ◽  
Onan Demirel ◽  
Irem Y. Tumer

Detection of potential failures and human error and their propagation over time at an early design stage will help prevent system failures and adverse accidents. Hence, there is a need for a failure analysis technique that will assess potential functional/component failures, human errors, and how they propagate to affect the system overall. Prior work has introduced FFIP (Functional Failure Identification and Propagation), which considers both human error and mechanical failures and their propagation at a system level at early design stages. However, it fails to consider the specific human actions (expected or unexpected) that contributed towards the human error. In this paper, we propose a method to expand FFIP to include human action/error propagation during failure analysis so a designer can address the human errors using human factors engineering principals at early design stages. To explore the capabilities of the proposed method, it is applied to a hold-up tank example and the results are coupled with Digital Human Modeling to demonstrate how designers can use these tools to make better design decisions before any design commitments are made.


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