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 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 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.

Semantic Interoperability of GD&T Data Through ISO 10303 Step AP242

2016 ◽  
Author(s):  
Adarsh Venkiteswaran ◽  
Sayed Mohammad Hejazi ◽  
Deepanjan Biswas ◽  
Jami J. Shah ◽  
Joseph K. Davidson
Keyword(s):  
Data Exchange ◽  
Smart Manufacturing ◽  
Context Model ◽  
Product Model ◽  
Shape Information ◽  
Computer Aided Process Planning ◽  
Model Based ◽  
Computer Aided ◽  
Iso 10303 ◽  
Manufacturing Applications

Industries are continuously trying to improve the time to market through automation and optimization of existing product development processes. Large companies vow to save significant time and resources through seamless communication of data between design, manufacturing, supply chain and quality assurance teams. In this context, Model Based Definition/Engineering (MBD) / (MBE) has gained popularity, particularly in its effort to replace traditional engineering drawings and documentations with a unified digital product model in a multi-disciplinary environment. Widely used 3D data exchange models (STEP AP 203, 214) contains mere shape information, which does not provide much value for reuse in downstream manufacturing applications. However, the latest STEP AP 242 (ISO 10303-242) “Managed model based 3D engineering” aims to support smart manufacturing by capturing semantic Product Manufacturing Information (PMI) within the 3D model and also helping with long-term archival. As a primary, for interoperability of Geometric Dimensions & Tolerances (GD&T) through AP 242, CAx Implementor Forum has published a set of recommended practices for the implementation of a translator. In line with these recommendations, this paper discusses the implementation of an AP 203 to AP 242 translator by attaching semantic GD&T available in an in-house Constraint Tolerance Graph (CTF) file. Further, semantic GD&T data can be automatically consumed by downstream applications such as Computer Aided Process Planning (CAPP), Computer Aided Inspection (CAI), Computer Aided Tolerance Systems (CATS) and Coordinate Measuring Machines (CMM). Also, this paper will briefly touch base on the important elements that will constitute a comprehensive product data model for model-based interoperability.

Automatic Integration of System-Level Design and System Optimization Based on SysML

2012 ◽  
Vol 249-250 ◽  
pp. 1154-1159
Author(s):  
Yu Sheng Liu ◽  
Wen Qiang Yuan
Keyword(s):  
System Design ◽  
Systems Engineering ◽  
Optimization Model ◽  
System Modeling ◽  
System Optimization ◽  
System Level ◽  
Design Parameters ◽  
System Level Design ◽  
Model Based ◽  
Level Design

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.

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

2004 ◽  
Author(s):  
Trevor Bailey ◽  
Suzanne Woll ◽  
Rajul Misra ◽  
Kevin Otto
Keyword(s):  
Systems Engineering ◽  
Best Practice ◽  
Success Factors ◽  
System Modeling ◽  
Root Cause Analysis ◽  
System Level ◽  
Cause Analysis ◽  
Root Cause ◽  
Model Based ◽  
Model Based Systems Engineering

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.

scholarly journals Integrated Instrumentation and Sensor Systems Enabling Condition-Based Maintenance of Aerospace Equipment

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Richard C. Millar
Keyword(s):  
Systems Engineering ◽  
Health Management ◽  
Condition Based Maintenance ◽  
System Level ◽  
Sensor Technology ◽  
Signal Acquisition ◽  
Sensor Systems ◽  
Engineering Approach ◽  
Architectural Framework ◽  
Functional Features

The objective of the work reported herein was to use a systems engineering approach to guide development of integrated instrumentation/sensor systems (IISS) incorporating communications, interconnections, and signal acquisition. These require enhanced suitability and effectiveness for diagnostics and health management of aerospace equipment governed by the principles of Condition-based maintenance (CBM). It is concluded that the systems engineering approach to IISS definition provided clear benefits in identifying overall system requirements and an architectural framework for categorizing and evaluating alternative architectures, relative to a bottom up focus on sensor technology blind to system level user needs. CBM IISS imperatives identified include factors such as tolerance of the bulk of aerospace equipment operational environments, low intrusiveness, rapid reconfiguration, and affordable life cycle costs. The functional features identified include interrogation of the variety of sensor types and interfaces common in aerospace equipment applications over multiplexed communication media with flexibility to allow rapid system reconfiguration to adapt to evolving sensor needs. This implies standardized interfaces at the sensor location (preferably to open standards), reduced wire/connector pin count in harnesses (or their elimination through use of wireless communications).

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.

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