scholarly journals Use of Bayesian Networks for Qualification Planning: A Predictive Analysis Framework for a Technically Complex Systems Engineering Problem

2015 ◽  
Vol 61 ◽  
pp. 133-140 ◽  
Author(s):  
Davinia B. Rizzo ◽  
Mark R. Blackburn
Keyword(s):  
Complex Systems ◽  
Bayesian Networks ◽  
Systems Engineering ◽  
Engineering Problem ◽  
Predictive Analysis ◽  
Analysis Framework

Model-Eliciting Activities: A Research-Based Method for Inquiry Learning and Professional Development in the Engineering Classroom

2008 ◽  
Author(s):  
Tamara J. Moore
Keyword(s):  
Complex Systems ◽  
Student Development ◽  
Engineering Problem ◽  
First Year ◽  
Foundation Engineering ◽  
Underrepresented Populations ◽  
Model Eliciting Activities ◽  
Engineering Problems ◽  
The Given ◽  
Mathematics And Science

Attracting students to engineering is a challenge. In addition, ABET requires that engineering graduates be able to work on multi-disciplinary teams and apply mathematics and science when solving engineering problems. One manner of integrating teamwork and engineering contexts in a first-year foundation engineering course is through the use of Model-Eliciting Activities (MEAs) — realistic, client-driven problems based on the models and modeling theoretical framework. A Model-Eliciting Activity (MEA) is a real-world client-driven problem. The solution of an MEA requires the use of one or more mathematical or engineering concepts that are unspecified by the problem — students must make new sense of their existing knowledge and understandings to formulate a generalizable mathematical model that can be used by the client to solve the given and similar problems. An MEA creates an environment in which skills beyond mathematical abilities are valued because the focus is not on the use of prescribed equations and algorithms but on the use of a broader spectrum of skills required for effective engineering problem-solving. Carefully constructed MEAs can begin to prepare students to communicate and work effectively in teams; to adopt and adapt conceptual tools; to construct, describe, and explain complex systems; and to cope with complex systems. MEAs provide a learning environment that is tailored to a more diverse population than typical engineering course experiences as they allow students with different backgrounds and values to emerge as talented, and that adapting these types of activities to engineering courses has the potential to go beyond “filling the gaps” to “opening doors” to women and underrepresented populations in engineering. Further, MEAs provide evidence of student development in regards to ABET standards. Through NSF-funded grants, multiple MEAs have been developed and implemented with a MSE-flavored nanotechnology theme. This paper will focus on the content, implementation, and student results of one of these MEAs.

scholarly journals A dynamic Bayesian network approach for prognosis computations on discrete state systems

2017 ◽  
Vol 231 (5) ◽  
pp. 516-533 ◽  
Author(s):  
Josquin Foulliaron ◽  
Laurent Bouillaut ◽  
Patrice Aknin ◽  
Anne Barros
Keyword(s):  
Complex Systems ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Multicomponent System ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Discrete State ◽  
Virtual Maintenance

The maintenance optimization of complex systems is a key question. One important objective is to be able to anticipate future maintenance actions required to optimize the logistic and future investments. That is why, over the past few years, the predictive maintenance approaches have been an expanding area of research. They rely on the concept of prognosis. Many papers have shown how dynamic Bayesian networks can be relevant to represent multicomponent complex systems and carry out reliability studies. The diagnosis and maintenance group from French institute of science and technology for transport, development and networks (IFSTTAR) developed a model (VirMaLab: Virtual Maintenance Laboratory) based on dynamic Bayesian networks in order to model a multicomponent system with its degradation dynamic and its diagnosis and maintenance processes. Its main purpose is to model a maintenance policy to be able to optimize the maintenance parameters due to the use of dynamic Bayesian networks. A discrete state-space system is considered, periodically observable through a diagnosis process. Such systems are common in railway or road infrastructure fields. This article presents a prognosis algorithm whose purpose is to compute the remaining useful life of the system and update this estimation each time a new diagnosis is available. Then, a representation of this algorithm is given as a dynamic Bayesian network in order to be next integrated into the Virtual Maintenance Laboratory model to include the set of predictive maintenance policies. Inference computation questions on the considered dynamic Bayesian networks will be discussed. Finally, an application on simulated data will be presented.

Evaluating the Systems Engineering Problem Management Process for Industrial Manufacturing Problems

2016 ◽  
Vol 19 (2) ◽  
pp. 133-145 ◽  
Author(s):  
Dennis A. Perry ◽  
Bill Olson ◽  
Paul Blessner ◽  
Timothy D. Blackburn
Keyword(s):  
Systems Engineering ◽  
Engineering Problem ◽  
Management Process ◽  
Problem Management ◽  
Industrial Manufacturing ◽  
Manufacturing Problems

scholarly journals Assessing Cyber-Worthiness of Complex System Capabilities using MBSE: A new rigorous engineering methodology

2021 ◽  
Author(s):  
Stuart Fowler ◽  
Keith Joiner ◽  
Elena Sitnikova
Keyword(s):  
Complex System ◽  
Complex Systems ◽  
Systems Engineering ◽  
Cyber Attacks ◽  
Simulation Methods ◽  
Assessment Methodology ◽  
Cyber Attack ◽  
Cyber Threats ◽  
Attack Surface ◽  
Model Based Systems Engineering

<div>Cyber-worthiness as it is termed in Australian Defence, or cyber-maturity more broadly, is a necessary feature of modern complex systems which are required to operate in a hostile cyber environment. To evaluate the cyber-worthiness of complex systems, an assessment methodology is required to examine a complex system’s or system-of-system’s vulnerability to and risk of cyber-attacks that can compromise such systems. This assessment methodology should address the cyber-attack surface and threat kill chains, including supply chains and supporting infrastructure. A cyber-worthiness capability assessment methodology has been developed based on model-based systems engineering concepts to analyse the cyber-worthiness of complex systems and present a risk assessment of various cyber threats to the complex system. This methodology incorporates modelling and simulation methods that provide organisations greater visibility and consistency across diverse systems, especially to drive cybersecurity controls, investment and operational decisions involving aggregated systems. In this paper, the developed methodology will be presented in detail and hypothesised outcomes will be discussed.</div>

scholarly journals ENGINEERING SUSTAINABLE COMPLEX SYSTEMS

2013 ◽  
Vol 4 (4) ◽  
pp. 4-14 ◽  
Author(s):  
Tareq Z. Ahram
Keyword(s):  
Complex Systems ◽  
Systems Engineering ◽  
New Technologies ◽  
Business Environment ◽  
Sustainable Business ◽  
Global Business ◽  
Two Factors ◽  
Critical Requirement ◽  
And Performance ◽  
Industrial Solutions

Abstract Given the most competitive nature of global business environment, effective engineering innovation is a critical requirement for all levels of system lifecycle development. The society and community expectations have increased beyond environmental short term impacts to global long term sustainability approach. Sustainability and engineering competence skills are extremely important due to a general shortage of engineering talent and the need for mobility of highly trained professionals [1]. Engineering sustainable complex systems is extremely important in view of the general shortage of resources and talents. Engineers implement new technologies and processes to avoid the negative environmental, societal and economic impacts. Systems thinking help engineers and designers address sustainable development issues with a global focus using leadership and excellence. This paper introduces the Systems Engineering (SE) methodology for designing complex and more sustainable business and industrial solutions, with emphasis on engineering excellence and leadership as key drivers for business sustainability. The considerable advancements achieved in complex systems engineering indicate that the adaptation of sustainable SE to business needs can lead to highly sophisticated yet widely useable collaborative applications, which will ensure the sustainability of limited resources such as energy and clean water. The SE design approach proves critical in maintaining skills needed in future capable workforce. Two factors emerged to have the greatest impact on the competitiveness and sustainability of complex systems and these were: improving skills and performance in engineering and design, and adopting SE and human systems integration (HSI) methodology to support sustainability in systems development. Additionally, this paper provides a case study for the application of SE and HSI methodology for engineering sustainable and complex systems.

Industry 4.0 From the Systems Engineering Perspective

2020 ◽  
pp. 575-599
Author(s):  
Vladimír Bureš
Keyword(s):  
Complex System ◽  
Physical Activity ◽  
Complex Systems ◽  
Frequency Analysis ◽  
Systems Engineering ◽  
Industry 4.0 ◽  
Control Level ◽  
Design Development ◽  
Starting Point ◽  
And Control

Systems engineering focuses on design, development, and implementation of complex systems. Not only does the Industry 4.0 concept consist of various technical components that need to be properly set and interconnected, but it is also tied to various managerial aspects. Thus, systems engineering approach can be used for its successful deployment. Overemphasis of technological aspects of Industry 4.0 represents the main starting point of this chapter. Then, collocation analysis, word clusters identification, selection and exemplification of selected domain in the business management realm, and frequency analysis are used in order to develop a holistic framework of Industry 4.0. This framework comprises six levels – physical, activity, outcome, content, triggers, and context. Moreover, the information and control level is integrated. The new holistic framework helps to consider Industry 4.0 from the complex systems engineering perspective – design and deployment of a complex system with required parameters and functionality.

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