Transient Root Cause Analysis: A Model-Based Systems Engineering Methodology

Author(s):  
Trevor Bailey ◽  
Suzanne Woll ◽  
Rajul Misra ◽  
Kevin Otto

This paper presents a model-based systems engineering methodology that can be applied to perform a root cause analysis on transient systems. The methodology extends existing root cause analysis best practice by incorporating system modeling and analysis techniques. The methodology is deployed through a detailed 5-step process to understand, identify, assess, FMEA, and validate potential transient system-level root causes. A transient performance reliability analysis for a dual mode refrigeration system is used to demonstrate how the methodology can be applied. The paper also describes a set of success factors for applying the methodology using a phased approach with a large cross-functional team.

2021 ◽  
Vol 1 ◽  
pp. 3369-3378
Author(s):  
Stephan Husung ◽  
Christian Weber ◽  
Atif Mahboob ◽  
Sven Kleiner

AbstractModel-Based Systems Engineering (MBSE) is an efficient approach to support product development in order to meet today's challenges. The MBSE approach includes methods and, above all, modelling approaches of the technical system with the aim of continuous use in development. The objective of this paper is to use the potential of the MBSE models and to show the added value of such models on the system level when used as a single source. With this objective, this paper presents a three-step approach to systematically identify and apply meaningful modelling approaches within MBSE, based on the needs during the development process. Furthermore, an FMEA example is included in this paper to elaborate the use of MBSE in the system failure analysis.


2012 ◽  
Vol 249-250 ◽  
pp. 1154-1159
Author(s):  
Yu Sheng Liu ◽  
Wen Qiang Yuan

Model based systems engineering (MBSE) is becoming a promising approach for the system-level design of complex mechatronics. And several MBSE tools are developed to conduct system modeling. However, the system design cannot be optimized in current MBSE tools. In this study, an approach is presented to conduct the task. A set of optimization stereotype is defined at first which is used to formalize the optimization model based on the system design model. Then the design parameters and their relationships applied optimization stereotypes are extracted and transferred to construct the tool-dependent optimization model. Finally, the optimization model is solved and the results are given back and then modify the corresponding system model automatically. In this paper, MagicDraw is used to model the whole system whereas Matlab optimizer is used for optimization. The combustion engine is chosen as the example to illustrate the proposed approach.


Author(s):  
Bence Hevesi

Abstract In this paper, different failure analysis (FA) workflows are showed which combines different FA approaches for fast and efficient fault isolation and root cause analysis in system level products. Two case studies will be presented to show the importance of a well-adjusted failure analysis workflow.


2019 ◽  
Vol 67 (3) ◽  
pp. 246-269 ◽  
Author(s):  
Huaxia Li ◽  
Minjie Zou ◽  
Georg Hogrefe ◽  
Daria Ryashentseva ◽  
Michael Sollfrank ◽  
...  

Abstract Due to the increasing integration of different disciplines, the complexity in the development of mechatronic production systems is growing. To address this issue, a multi-disciplinary design approach has been proposed, which follows the model-based systems engineering (MBSE) architecture and integrates the interdisciplinary modeling approach SysML4Mechatronics. In this article, the applicability of this approach in the machine and plant manufacturing domain is demonstrated using five use cases. These use cases are derived from industry and are demonstrated in a lab-sized production plant. The results of the application show that the approach can completely fulfil the proposed industrial requirements, namely interdisciplinary modeling, comprehensibility of system modeling, reusability of the modeling components, coupling different engineering models and checking data consistency.


2020 ◽  
Vol 14 (3) ◽  
pp. 4165-4175
Author(s):  
Ryan A. Colletti ◽  
Ahsan Qamar ◽  
Sandro P. Nuesch ◽  
Christiaan J. J. Paredis

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