Resilience Modeling and Quantification for Design of Complex Engineered Systems Using Bayesian Networks

2014 ◽  
Author(s):  
Seyedmohsen Hosseini ◽  
Nita Yodo ◽  
Pingfeng Wang
Keyword(s):  
Supply Chain ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Conceptual Framework ◽  
Electric Motor ◽  
Qualitative Assessment ◽  
Complex Engineered Systems ◽  
Engineered Systems ◽  
Quantitative Tool

The concept of engineering resilience has received prevalent attention from academia as well as industry because it contributes a new means of thinking about how to withstand against disruptions and recover properly from them. Although the concept of resilience was scholarly explored in diverse disciplines, there are only few which focus on how to quantitatively measure the engineering resilience. This paper is dedicated to explore the gap between quantitative and qualitative assessment of engineering resilience in the domain of design of complex engineered systems. A conceptual framework is first proposed for the modeling of engineering resilience, and then Bayesian network is employed as a quantitative tool for the assessment and analysis of engineering resilience for complex systems. A case study related to electric motor supply chain is employed to demonstrate the proposed approach. The proposed resilience quantification and analysis approach using Bayesian networks would empower system designers to have a better grasp of the weakness and strength of their own systems against system disruptions induced by adverse failure events.

Resilience Modeling and Quantification for Engineered Systems Using Bayesian Networks

2016 ◽  
Vol 138 (3) ◽  
Author(s):  
Nita Yodo ◽  
Pingfeng Wang
Keyword(s):  
Supply Chain ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Conceptual Framework ◽  
Production Process ◽  
Case Studies ◽  
Industrial Applications ◽  
Qualitative Assessment ◽  
Engineered Systems ◽  
Quantitative Tool

The concept of engineering resilience has received a prevalent attention from academia as well as industry because it contributes a new means of thinking about how to withstand against disruptions and recover properly. Although the concept of resilience was scholarly explored in diverse disciplines, there are only few which focus on how to quantitatively measure the engineering resilience. This paper is dedicated to explore the gap between quantitative and qualitative assessment of engineering resilience in the domain of designing engineered systems in industrial applications. A conceptual framework is first proposed for modeling engineering resilience, and then Bayesian network (BN) is employed as a quantitative tool for the assessment and analysis of the resilience for engineered systems. Two industrial-based case studies, supply chain and production process, are employed to demonstrate the proposed approach. The proposed resilience quantification and analysis approach using BNs would empower system designers to have a better grasp of the weakness and strength of their own systems against system disruptions induced by adverse failure events.

Resilience Analysis and Allocation for Complex Systems Using Bayesian Network

2015 ◽  
Author(s):  
Nita Yodo ◽  
Pingfeng Wang
Keyword(s):  
Complex Systems ◽  
Bayesian Network ◽  
Industrial Applications ◽  
Engineering System ◽  
Supply Chain System ◽  
Complex Engineered Systems ◽  
Engineered Systems ◽  
Engineering System Design ◽  
Allocation Approach

The concept of resilience has been explored in diverse disciplines. However, there are only a few which focus on how to quantitatively measure engineering resilience and allocate resilience in engineering system design. This paper is dedicated to exploring the gap between quantitative and qualitative assessments of engineering resilience in the domain of designing complex engineered systems, thus optimally allocating resilience into subsystems and components level in industrial applications. A conceptual framework is first proposed for modeling engineering resilience, and then Bayesian Network is employed as a quantitative tool for the assessment and analysis of engineering resilience for complex systems. One industrial-based case study, a supply chain system, is employed to demonstrate the proposed approach. The proposed resilience quantification and allocation approach using Bayesian Networks would empower system designers to have a better grasp of the weakness and strength of their own systems against system disruptions induced by adverse failure events.

scholarly journals Forensic Engineering Use Of Fault Tree Analysis

2006 ◽  
Vol 23 (2) ◽  
Author(s):  
Frank H. Johnson ◽  
DeWitt William E.
Keyword(s):  
Fault Tree ◽  
Fault Tree Analysis ◽  
Tree Analysis ◽  
Complex Engineered Systems ◽  
Track Record ◽  
Forensic Engineering ◽  
Fire And Explosion ◽  
Engineered Systems ◽  
Analytical Tools

Analytical Tools, Like Fault Tree Analysis, Have A Proven Track Record In The Aviation And Nuclear Industries. A Positive Tree Is Used To Insure That A Complex Engineered System Operates Correctly. A Negative Tree (Or Fault Tree) Is Used To Investigate Failures Of Complex Engineered Systems. Boeings Use Of Fault Tree Analysis To Investigate The Apollo Launch Pad Fire In 1967 Brought National Attention To The Technique. The 2002 Edition Of Nfpa 921, Guide For Fire And Explosion Investigations, Contains A New Chapter Entitled Failure Analysis And Analytical Tools. That Chapter Addresses Fault Tree Analysis With Respect To Fire And Explosion Investigation. This Paper Will Review The Fundamentals Of Fault Tree Analysis, List Recent Peer Reviewed Papers About The Forensic Engineering Use Of Fault Tree Analysis, Present A Relevant Forensic Engineering Case Study, And Conclude With The Results Of A Recent University Study On The Subject.

scholarly journals In pursuit of supply chain fit

2019 ◽  
Vol 30 (3) ◽  
pp. 821-844 ◽  
Author(s):  
Yasmine Sabri
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Firm Performance ◽  
Conceptual Framework ◽  
Organisational Structure ◽  
Content Type ◽  
Supply Chain Strategy ◽  
Corporate Groups ◽  
The Relationship

Purpose The purpose of this paper is to develop exploratory propositions and a conceptual framework on the interaction between organisational structure (decision-making centralisation and internal coordination) and the relationship between supply chain fit and firm performance. Design/methodology/approach Through a case study, two corporate groups with distinctive organisational structures were examined; both are undergoing a critical moment of changes to their top management and are reshaping their corporate and supply chain strategies. Data on decision-making centralisation, internal coordination mechanisms, supply, demand and innovation uncertainties, and supply chain strategies were collected from key respondents. Findings The analysis conducted suggests the need to consider the joint interaction between organisational structure and supply chain fit in offsetting the implications of a potential misfit on firm performance. Furthermore, the context sensitivity of a supply chain is often overlooked, hence simply modifying supply chain strategy does not necessarily lead to a variation in firm performance. Practical implications This research is of particular importance to most organisations in the testing times of uncertainty in the global landscape. It guides supply chain practitioners to better understand which elements of the organisational structure interact with the uncertainty of supply, demand and innovation. Originality/value This paper is one of the first to investigate the interaction between elements of organisational structure and supply chain fit and identify decision-making centralisation and coordination as the internal uncertainty factors that are most relevant to supply chain fit research. A conceptual framework has been built for future testing, in which the organisational structure moderates the relationship between supply chain fit and firm performance.

scholarly journals Resiliency analysis for complex engineered system design

2015 ◽  
Vol 29 (1) ◽  
pp. 93-108 ◽  
Author(s):  
Hoda Mehrpouyan ◽  
Brandon Haley ◽  
Andy Dong ◽  
Irem Y. Tumer ◽  
Christopher Hoyle
Keyword(s):  
Electrical Power ◽  
Nonlinear Dynamical System ◽  
Operating Environment ◽  
Electrical Power System ◽  
Nonlinear Dynamical ◽  
Complex Engineered Systems ◽  
Failure Propagation ◽  
Engineered Systems ◽  
Mean Time

AbstractResilience is a key driver in the design of systems that must operate in an uncertain operating environment, and it is a key metric to assess the capacity for systems to perform within the specified performance envelop despite disturbances to their operating environment. This paper describes a graph spectral approach to calculate the resilience of complex engineered systems. The resilience of the design architecture of complex engineered systems is deduced from graph spectra. This is calculated from adjacency matrix representations of the physical connections between components in complex engineered systems. Furthermore, we propose a new method to identify the most vulnerable components in the design and design architectures that are robust to transmission of failures. Nonlinear dynamical system and epidemic spreading models are used to compare the failure propagation mean time transformation. Using these metrics, we present a case study based on the Advanced Diagnostics and Prognostics Testbed, which is an electrical power system developed at NASA Ames as a subsystem for the ramp system of an infantry fighting vehicle.

scholarly journals ARCHITECTING COMPLEX ENGINEERED SYSTEMS

2020 ◽  
Vol 1 ◽  
pp. 2415-2424
Author(s):  
U. Sellgren ◽  
D. Williamsson
Keyword(s):  
Interaction Strength ◽  
Strength Model ◽  
Clustering Method ◽  
Complex Engineered Systems ◽  
Heterogeneous Technologies ◽  
Points Of View ◽  
Engineered Systems ◽  
Physical Components ◽  
Novel Products

AbstractNovel products are commonly realized by integrating heterogeneous technologies. Product architecting focus on defining the scheme by which the product functions are allocated to physical components. A DSM-based clustering method that integrates technical complexity and strategic concerns has previously been proposed. It has been shown that interaction weights in the DSM may affect the clustering result. A complexity-based interaction strength model to be used in DSM clustering is proposed here. The case study gives promising results from both interaction performance and safety points of view.

Digital transformations and supply chain management: a Lean Six Sigma perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huay Ling Tay ◽  
Hui Shan Loh
Keyword(s):  
Supply Chain ◽  
Problem Solving ◽  
Supply Chains ◽  
Conceptual Framework ◽  
Lean Six Sigma ◽  
Massive Data ◽  
Content Type ◽  
Study Approach ◽  
Shed Light

Purpose With the advent of technological connectivity and access to massive data, the possibilities of augmenting Lean Six Sigma’s Define-Measure-Analyze-Improve-Control (LSS’s DMAIC) problem-solving approach with advanced technologies are enormous. This paper aims to examine digital transformations (DT) of supply chains from a process improvement angle using the LSS DMAIC approach. Design/methodology/approach This paper uses a case study approach. Three exemplary case studies were examined to shed light on how LSS can aid in DT to achieve enterprise-wide improvements and enhance value across the supply chains. Findings The paper provides a conceptual framework based on a structured DMAIC problem-solving approach to drive improvements in supply chains. The conceptual framework also provides a systematic approach for big data to be integrated in LSS initiatives to achieve greater supply chain performance. Originality/value This paper extends research in LSS supply chains by providing a guidance through a conceptual framework that integrate DT and LSS supply chains to support successful digital transformation and LSS supply chains.

Curriculum Reform Due to Project-Based Learning Methodology Implementation in Teaching an ESP Course to Russian University Natural Science Students

2019 ◽  
pp. 250-269
Author(s):  
Boris A. Zhigalev ◽  
Olga A. Obdalova ◽  
Ludmila Y. Minakova
Keyword(s):  
Conceptual Framework ◽  
Curriculum Reform ◽  
Natural Science ◽  
Project Based Learning ◽  
Qualitative Assessment ◽  
Science Students ◽  
Target Skills ◽  
The University ◽  
Methodological Aspects

This chapter investigates the didactic potential of a project-based learning (PjBL) methodology as a means of modernizing curriculum and improving an ESP course at the university level. The chapter outlines in detail the main elements of the negotiated teaching framework and discusses why such a methodology is appropriate for teaching communicative competence and developing learners' performance in English in a professional context. It also presents practical devices for the implementation of the designed conceptual framework. The first part of the chapter focuses on outlining methodological aspects of the undertaken case study. The second part concentrates on the quantitative and qualitative assessment of the results of the PjBL framework intervention. Within this context, a discussion about the influence of the PjBL course implementation on 48 students is provided. The authors also specify some factors contributing to the effectiveness of the development of learners' target skills attributed to the introduction of the featured model for the ESP project-based course.

A Petri Net-Based Tool for the Analysis of Generalized Continuous Time Bayesian Networks

2013 ◽  
pp. 118-143 ◽  
Author(s):  
Daniele Codetta-Raiteri ◽  
Luigi Portinale
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Petri Net ◽  
Continuous Time ◽  
Dynamic Bayesian Network ◽  
Software Tool ◽  
Inference Algorithms ◽  
Continuous Time Bayesian Networks ◽  
Generalized Stochastic Petri Net

A software tool for the analysis of Generalized Continuous Time Bayesian Networks (GCTBN) is presented. GCTGBN extend CTBN introducing in addition to continuous time-delayed variables, non-delayed or “immediate” variables. The tool is based on the conversion of a GCTBN model into a Generalized Stochastic Petri Net (GSPN), which is an actual mean to perform the inference (analysis) of the GCTBN. Both the inference tasks (prediction and smoothing) can be performed in this way. The architecture and the methodologies of the tool are presented. In particular, the conversion rules from GCTBN to GSPN are described, and the inference algorithms exploiting GSPN transient analysis are presented. A running example supports their description: a case study is modelled as a GCTBN and analyzed by means of the tool. The results are verified by modelling and analyzing the system as a Dynamic Bayesian Network, another form of Bayesian Network, assuming discrete time.

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