The Structure of Vulnerable Nodes in Behavioral Network Models of Complex Engineered Systems

2017 ◽  
Author(s):  
Hannah S. Walsh ◽  
Andy Dong ◽  
Irem Y. Tumer
Keyword(s):  
Network Models ◽  
Design Parameters ◽  
Engineering Systems ◽  
Critical Design ◽  
Complex Engineered Systems ◽  
Design Variables ◽  
Engineered Systems ◽  
Process Controls ◽  
Robustness Metrics

All methods associated with failure analysis attempt to identify critical design variables and parameters such that appropriate process controls can be implemented to detect problems before they occur. This paper introduces a new approach to the identification of critical design variables and parameters through the concept of bridging nodes. Using a network-based perspective in which design parameters and variables are modeled as nodes, results show that vulnerable parameters tend to be bridging nodes, which are nodes that connect two or more groups of nodes that are organized together in order to perform an intended function. This paper extends existing modeling capabilities based upon a behavioral network analysis (BNA) approach and presents empirical results identifying the relationship between bridging nodes and parameter vulnerability as determined by existing, network metric-based methods. These topological network robustness metrics were used to analyze a large number of engineering systems. Bridging nodes are associated with significantly larger changes in network degradation, as measured by these metrics, than non-bridging nodes when subject to attack (p < 0.001). The results indicate the structural role of vulnerable design parameters in a behavioral network.

scholarly journals The role of bridging nodes in behavioral network models of complex engineered systems

2018 ◽  
Vol 4 ◽  
Author(s):  
Hannah S. Walsh ◽  
Andy Dong ◽  
Irem Y. Tumer
Keyword(s):  
Early Stage ◽  
Network Models ◽  
Vital Role ◽  
System Level ◽  
Complex Engineered Systems ◽  
Failure Tolerance ◽  
Design Variables ◽  
Engineered Systems ◽  
Network Metrics

Recent advances in early stage failure analysis approaches have introduced behavioral network analysis (BNA), which applies a network-based model of a complex engineered system to detect the system-level effect of ‘local’ failures of design variables and parameters. Previous work has shown that changes in microscale network metrics can signify system-level performance degradation. This article introduces a new insight into the influence of the community structure of the behavioral network on the failure tolerance of the system through the role of bridging nodes. Bridging nodes connect a community of nodes in a system to one or more nodes or communities outside of the community. In a study of forty systems, it is found that bridging nodes, under attack, are associated with significantly larger system-level behavioral degradation than non-bridging nodes. This finding indicates that the modularity of the behavioral network could be key to understanding the failure tolerance of the system and that parameters associated with bridging nodes between modules could play a vital role in system degradation.

Creating Faultable Network Models of Complex Engineered Systems

2014 ◽  
Author(s):  
Brandon M. Haley ◽  
Andy Dong ◽  
Irem Y. Tumer
Keyword(s):  
Failure Analysis ◽  
Network Models ◽  
System Behavior ◽  
Levels Of Abstraction ◽  
Complex Engineered Systems ◽  
System A ◽  
Topological Configuration ◽  
Engineered Systems ◽  
Types Of Faults ◽  
Insight Into

This paper presents a new methodology for modeling complex engineered systems using complex networks for failure analysis. Many existing network-based modeling approaches for complex engineered systems “abstract away” the functional details to focus on the topological configuration of the system and thus do not provide adequate insight into system behavior. To model failures more adequately, we present two types of network representations of a complex engineered system: a uni-partite architectural network and a weighted bi-partite behavioral network. Whereas the architectural network describes physical inter-connectivity, the behavioral network represents the interaction between functions and variables in mathematical models of the system and its constituent components. The levels of abstraction for nodes in both network types affords the evaluation of failures involving morphology or behavior, respectively. The approach is shown with respect to a drivetrain model. Architectural and behavioral networks are compared with respect to the types of faults that can be described. We conclude with considerations that should be employed when modeling complex engineered systems as networks for the purpose of failure analysis.

Designing Robust Systems Using Bioinspired Product Architecture

2021 ◽  
Author(s):  
Devesh Bhasin ◽  
David Staack ◽  
Daniel A. McAdams
Keyword(s):  
Biological Systems ◽  
Product Architecture ◽  
Engineering Systems ◽  
Modular Product ◽  
Engineered Systems ◽  
Function Sharing ◽  
Robust Systems ◽  
Random Failures

Abstract This work analyzes the role of bioinspired product architecture in facilitating the development of robust engineering systems. Existing studies on bioinspired product architecture largely focus on inspiring biology-like function-sharing in engineering design. This work shows that the guidelines for bioinspired product architecture, originally developed for bioinspiration of function-sharing, may induce robustness to random failures in engineered systems. To quantify such an improvement, this study utilizes Functional Modeling to derive modular equivalents of biological systems. The application of the bioinspired product architecture guidelines is then modeled as a transition from the modular product architecture of the modular equivalents to the actual product architecture of the biological systems. The robustness of the systems to random failures is analyzed after the application of each guideline by modeling the systems as directed networks. A singular robustness metric is then introduced to quantify the degradation in the expected functionality of systems upon increasing severity of random disruptions. Our results show that a system with bioinspired product architecture exhibits a gradual degradation in expected functionality upon increasing the number of failed modules as compared to an equivalent system with a one-to-one mapping of functions to modules. The findings are validated by designing and analyzing a COVID-19 breathalyzer as a case study.

The Value of Flexible Capacity Expansion Strategies in Design of Complex Decentralized Engineering Systems

2015 ◽  
Author(s):  
Junfei Hu ◽  
Michel-Alexandre Cardin
Keyword(s):  
Economies Of Scale ◽  
Unit Cost ◽  
Waste To Energy ◽  
Small Scale ◽  
Engineering Systems ◽  
Complex Engineered Systems ◽  
Decentralized Design ◽  
Engineered Systems ◽  
Reduce Unit Cost ◽  
Over Time

This paper presents and applies a simulation-based methodology to assess the value of flexible decentralized engineering systems (i.e., the ability to flexibly expand the capacity in multiple sites over time and space). This work differs from others by analyzing explicitly the tradeoffs between economies of scale (EoS) — which favors building large capacity upfront to reduce unit cost and accommodate high anticipated demand — and the time value of money — which favors deferring capacity investments to the future and deploying smaller modules to reduce unit cost. The study aims to identify the best strategies to deploy capacity of complex engineered systems over time and improve their economic lifecycle performance in the face of uncertainty. This study is illustrated using a waste-to-energy system operated in Singapore. The results show that a decentralized design with the real option to expand the capacity in different locations and times improves the expected net present value by more than 20% under the condition of economies of scale α = 0.8 and discount rate λ = 8%, as compared to a fixed centralized design. The results also indicate that a flexible decentralized design outperforms other rigid designs under certain circumstances since it not only reduces transportation costs, but also has the advantage of flexible deployment strategies, such as deferring investment and avoiding unnecessary capacity. The results help designers and managers better compare centralized and decentralized design opportunities and to recognize the value of flexible decentralized designs in small-scale urban environments. The example also provides guidance for applying flexibility to a wider range of complex engineered systems and to determine the best strategies for deploying the capacity of systems in other urban contexts.

Steady-State Operability in Design for Dynamic Management in Realization of Networked Engineering Systems

2018 ◽  
Author(s):  
Jelena Milisavljevic Syed ◽  
Sesh Commuri ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Design Parameters ◽  
Engineering Systems ◽  
Review Of Literature ◽  
Dynamic Changes ◽  
Dynamic Management ◽  
Design Time ◽  
Key Features ◽  
Per Se ◽  
Engineered Systems

Digitization of networked engineered systems is a technology that is increasingly being adopted to respond to changes in the market. Hence, the need for design methods to design a system adaptable to dynamic changes in the market. It is evident through a critical review of literature that current approaches for designing of networked engineering systems are neither agile nor rapidly configurable, and, at design time, usually do not have flexibility in selection and determination of the values of design parameters to lower the overall cost during the execution of networked engineering systems and simultaneously ensure that the product quality is acceptable. Accordingly, in this paper, a method, Design for Dynamic Management, for the realization of networked engineering systems is proposed. The key features of this method, in the context of Design for Dynamic Management, are adaptability and operability in the design of systems. The efficacy of the method is illustrated on a 2-D panel stamping process. Our focus is on the method rather than the results per se.

scholarly journals HOW INDUSTRY 4.0 RESHAPES THE WORLD: RECOMMENDATIONS BASED ON COMPLEX GRAPH NETWORK ANALYSIS

2021 ◽  
Vol 1 ◽  
pp. 1755-1764
Author(s):  
Rongyan Zhou ◽  
Julie Stal-Le Cardinal
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Network Models ◽  
Global Supply Chain ◽  
Great Opportunity ◽  
Strategic Research ◽  
Complex Graph ◽  
The World ◽  
Tremendous Challenge

Abstract Industry 4.0 is a great opportunity and a tremendous challenge for every role of society. Our study combines complex network and qualitative methods to analyze the Industry 4.0 macroeconomic issues and global supply chain, which enriches the qualitative analysis and machine learning in macroscopic and strategic research. Unsupervised complex graph network models are used to explore how industry 4.0 reshapes the world. Based on the in-degree and out-degree of the weighted and unweighted edges of each node, combined with the grouping results based on unsupervised learning, our study shows that the cooperation groups of Industry 4.0 are different from the previous traditional alliances. Macroeconomics issues also are studied. Finally, strong cohesive groups and recommendations for businessmen and policymakers are proposed.

A unified framework for the design of low-complexity wavelet filters

2017 ◽  
Vol 15 (06) ◽  
pp. 1750054 ◽  
Author(s):  
Ameya K. Naik ◽  
Raghunath S. Holambe
Keyword(s):  
Feature Extraction ◽  
Compact Support ◽  
Low Complexity ◽  
Design Parameters ◽  
Unified Framework ◽  
Vanishing Moments ◽  
Wavelet Filters ◽  
Design Variables ◽  
Optimum Filter ◽  
Simulation Results

An outline is presented for construction of wavelet filters with compact support. Our approach does not require any extensive simulations for obtaining the values of design variables like other methods. A unified framework is proposed for designing halfband polynomials with varying vanishing moments. Optimum filter pairs can then be generated by factorization of the halfband polynomial. Although these optimum wavelets have characteristics close to that of CDF 9/7 (Cohen-Daubechies-Feauveau), a compact support may not be guaranteed. Subsequently, we show that by proper choice of design parameters finite wordlength wavelet construction can be achieved. These hardware friendly wavelets are analyzed for their possible applications in image compression and feature extraction. Simulation results show that the designed wavelets give better performances as compared to standard wavelets. Moreover, the designed wavelets can be implemented with significantly reduced hardware as compared to the existing wavelets.

CAD and Optimization of Helical Torsion Springs

1994 ◽  
Author(s):  
Sayed M. Metwalli ◽  
M. Alaa Radwan ◽  
Abdel Aziz M. Elmeligy
Keyword(s):  
Iterative Process ◽  
Minimum Weight ◽  
Design Parameters ◽  
Optimal Parameters ◽  
Direct Evaluation ◽  
Torsion Spring ◽  
Performance Requirements ◽  
Design Variables ◽  
Method Of Lagrange Multipliers ◽  
Torsion Springs

Abstract The convensional procedure of helical torsion spring design is an iterative process because of large number of requirements and relations that are to be attained once at a time. The design parameters are varied at random until the spring design satisfies performance requirements. A CAD of the spring for minimum weight is formulated with and without the variation of the maximum normal stress with the wire diameter. The CAD program solves by employing the method of Lagrange-Multipliers. The optimal parameters, in a closed form are obtained, normalized and plotted. These explicit relations of design variables allow direct evaluation of optimal design objective and hence, an absolute optimum could be achieved. The comparison of optimum results with those previously published, shows a pronounced achievement in the reduction of torsion spring weight.

A Study on Optimum Design Using Fuzzy Numbers As Design Variables

1995 ◽  
Author(s):  
Masao Arakawa ◽  
Hiroshi Yamakawa
Keyword(s):  
Fuzzy Sets ◽  
Optimum Design ◽  
Fuzzy Numbers ◽  
Fuzzy Optimization ◽  
Design Parameters ◽  
Numerical Examples ◽  
Design Variables ◽  
Conventional Methods

Abstract In this study, we summerize the method of fuzzy optimization using fuzzy numbers as design variables. In order to detect flaw in fuzzy calculation, we use LR-fuzzy numbers, which is known as its simplicity in calculation. We also use simple fuzzy numbers’ operations, which was proposed in the previous papers. The proposed method has unique characteristics that we can obtain fuzzy sets in design variables (results of the design) directly from single numerical optimizing process. Which takes a large number of numerical optimizing processes when we try to obtain similar results in the conventional methods. In the numerical examples, we compare the proposed method with several other methods taking imprecision in design parameters into account, and demonstrate the effectiveness of the proposed method.

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