scholarly journals Digital Twin-Driven Process and Equipment FMECA Generation for Smart Manufacturing Applications

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jay Meyer ◽  
Venkat Malepati ◽  
Caleb Hudson ◽  
Somnath Deb ◽  
...  
Keyword(s):  
Systems Engineering ◽  
Failure Modes ◽  
Fault Management ◽  
Automatic Generation ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Model Based ◽  
Manufacturing Applications ◽  
Criticality Analysis ◽  
Model Based Systems Engineering

Qualtech Systems, Inc. (QSI)’s integrated tool set, consisting of TEAMS-Designer® and TEAMS-RDS® provides a comprehensive digital twin-driven and model-based systems engineering approach that can be deployed for fault management throughout the equipment life-cycle – from its design for fault management to condition-based maintenance of the deployed equipment. In this paper, we present QSI’s approach towards adapting and enhancing their existing model-based systems engineering (MBSE) approach towards a comprehensive digital twin that incorporates constructs necessary for development of a Process Failure Modes and Criticality Analysis (P-FMECA) and integrates that with an Equipment FMECA. The paper will discuss the various levels of automation towards incorporation of these model constructs and their reuse towards automation of the development of the different digital twins and subsequently the automatic generation of the combined Process and Equipment FMECA. This automated ability to develop the integrated FMECA that incorporates both Process-level Failure Modes and Equipment-level Failure Modes allows the system designer and operators to correlate and identify process failures down to their root causes at the equipment-level and thereby producing a comprehensive actionable systems-level view of the entire Smart Manufacturing facility from a fault management design and operations perspective. The paper will present the application of this novel technology for the Advanced Metal Finishing Facility (AMFF) at the Warner-Robins Air Logistics Complex (WR-ALC) in Robins Air Force Base, Georgia, as part of WR-ALC’s initiative towards model-based enterprise (MBE) and smart manufacturing.

scholarly journals An Integrated model-based Approach for FMECA Development for Smart Manufacturing Applications

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Sudipto Ghoshal ◽  
Somnath Deb ◽  
Deepak Haste ◽  
Andrew Hess ◽  
Feraidoon Zahiri ◽  
...  
Keyword(s):  
Systems Engineering ◽  
Fault Management ◽  
Failure Detection ◽  
Condition Based Maintenance ◽  
System Level ◽  
Smart Manufacturing ◽  
Subsequent Paper ◽  
Model Based ◽  
Manufacturing Applications ◽  
Criticality Analysis

Qualtech Systems, Inc. (QSI)’s integrated tool set, consisting of TEAMS-Designer® and TEAMS-RDS® provides a comprehensive model-based systems engineering approach that can be deployed for fault management throughout the equipment life-cycle – from its design for fault management to condition-based maintenance of the equipment. The TEAMS® failure-cause effect dependency model is a digital twin representation of the equipment in its failure-space and allows for various types of analyses such as testability, serviceability, failure propagation and others that facilitate fault management design of the equipment. The same model is deployed through TEAMS-RDS® for condition monitoring, prognostics, real-time health assessment, failure impact analysis, guided troubleshooting and others that facilitate condition-based maintenance as well as ensure efficient and rapid maintenance actions. In this paper, we present an overview of QSI’s integrated toolset, with a focus on a systematic model-based approach towards an automated development of Failure Effects and Criticality Analysis (FMECA) and other relevant analyses for the equipment, for an improved understanding of failure effects and their causality at the system-level. The eventual objective here is improved equipment design as well as designing improved failure detection, failure isolation and failure mitigation. The paper will also discuss examples of such real-world applications for smart manufacturing in major depot maintenance facilities in the US. A subsequent paper will focus on the development and integration of process-level and equipment-level FMECAs for Smart Manufacturing applications.

scholarly journals Leveraging Digital Twin Technology in Model-Based Systems Engineering

Systems ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 7 ◽  
Author(s):  
Azad Madni ◽  
Carla Madni ◽  
Scott Lucero
Keyword(s):  
Systems Engineering ◽  
Physical System ◽  
System Simulation ◽  
Virtual Prototype ◽  
Digital Representation ◽  
Digital Twin ◽  
Model Based ◽  
Digital Twins ◽  
Status Data ◽  
Model Based Systems Engineering

Digital twin, a concept introduced in 2002, is becoming increasingly relevant to systems engineering and, more specifically, to model-based system engineering (MBSE). A digital twin, like a virtual prototype, is a dynamic digital representation of a physical system. However, unlike a virtual prototype, a digital twin is a virtual instance of a physical system (twin) that is continually updated with the latter’s performance, maintenance, and health status data throughout the physical system’s life cycle. This paper presents an overall vision and rationale for incorporating digital twin technology into MBSE. The paper discusses the benefits of integrating digital twins with system simulation and Internet of Things (IoT) in support of MBSE and provides specific examples of the use and benefits of digital twin technology in different industries. It concludes with a recommendation to make digital twin technology an integral part of MBSE methodology and experimentation testbeds.

Informationsqualität in der Produktentwicklung: Modellbasiertes Systems Engineering mit expliziter Berücksichtigung von Unsicherheit/Information Quality in Product Engineering: Model-Based Systems Engineering in Explicit Consideration of Uncertainty

Konstruktion ◽  
2020 ◽  
Vol 72 (11-12) ◽  
pp. 76-83
Author(s):  
Jens Pottebaum ◽  
Iris Gräßler
Keyword(s):  
Systems Engineering ◽  
Information Quality ◽  
Engineering Model ◽  
Model Based ◽  
Product Engineering ◽  
Explicit Consideration ◽  
Model Based Systems Engineering

Inhalt Unscharfe Anforderungen, verschiedene Lösungs-alternativen oder eingeschränkt gültige Simulationsmodelle sind Beispiele für inhärente Unsicherheit in der Produktentwicklung. Im vorliegenden Beitrag wird ein modellbasierter Ansatz vorgestellt, der das industriell etablierte Denken in Sicherheitsfaktoren um qualitative Aspekte ergänzt. Modelle der Informationsqualität helfen, die Unsicherheit von Ent- wicklungsartefakten beschreibend zu charakterisieren. Mittels semantischer Technologien wird Unsicherheit so wirklich handhabbar – nicht im Sinne einer Berechnung, sondern im Sinne einer qualitativen Interpretation. Dadurch entsteht wertvolles Wissen für die iterative Anforderungsanalyse, die Bewertung alternativer System-Architekturen oder für die Rekonfiguration von Simulationen.

scholarly journals An Approach to Complement Model-Based Vehicle Development by Implementing Future Scenarios

2021 ◽  
Vol 12 (3) ◽  
pp. 97
Author(s):  
Christian Raulf ◽  
Moritz Proff ◽  
Tobias Huth ◽  
Thomas Vietor
Keyword(s):  
Systems Engineering ◽  
Holistic Approach ◽  
Future Scenarios ◽  
Worst Case ◽  
System Architectures ◽  
Vehicle Development ◽  
Model Based ◽  
Scenario Technique ◽  
Stakeholder Needs ◽  
Model Based Systems Engineering

Today, vehicle development is already in a process of substantial transformation. Mobility trends can be derived from global megatrends and have a significant influence on the requirements of the developed vehicles. The sociological, technological, economic, ecological, and political developments can be determined by using the scenario technique. The results are recorded in the form of differently shaped scenarios; however, they are mainly document-based. In order to ensure a holistic approach in the sense of model-based systems engineering and to be able to trace the interrelationships of the fast-changing trends and requirements, it is necessary to implement future scenarios in the system model. For this purpose, a method is proposed that enables the consideration of future scenarios in model-based vehicle development. The procedure of the method is presented, and the location of the future scenarios within the system architectures is named. The method is applied and the resulting system views are derived based on the application example of an autonomous people mover. With the help of the described method, it is possible to show the effects of a change of scenario (e.g., best-case and worst-case) and the connections with the highest level of requirements: stakeholder needs.

Implementation Strategy for Seamless Model-based Systems Engineering

ATZ worldwide ◽  
2021 ◽  
Vol 123 (7-8) ◽  
pp. 66-71
Author(s):  
Thorsten Weyer ◽  
Marcel Goger ◽  
Walter Koch ◽  
Birgit Kremer
Keyword(s):  
Systems Engineering ◽  
Implementation Strategy ◽  
Model Based ◽  
Model Based Systems Engineering

scholarly journals USING MODEL-BASED SYSTEMS ENGINEERING FOR NEED-BASED AND CONSISTENT SUPPORT OF THE DESIGN PROCESS

2021 ◽  
Vol 1 ◽  
pp. 3369-3378
Author(s):  
Stephan Husung ◽  
Christian Weber ◽  
Atif Mahboob ◽  
Sven Kleiner
Keyword(s):  
Systems Engineering ◽  
Development Process ◽  
Added Value ◽  
System Level ◽  
System Failure ◽  
Model Based ◽  
Continuous Use ◽  
Model Based Systems Engineering ◽  
Consistent Support ◽  
Modelling Approaches

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.

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