A fuzzy clustering methodology to analyze interfaces and assess integration risks in large-scale systems

“Interface analysis and integration risk assessment for a large-scale, complex system is a difficult systems engineering task, but critical to the success of engineering systems with extraordinary capabilities. When dealing with large-scale systems there is little time for data gathering and often the analysis can be overwhelmed by unknowns and sometimes important factors are not measurable because of the complexities of the interconnections within the system. This research examines the significance of interface analysis and management, identifies weaknesses in literature on risk assessment for a complex system, and exploits the benefits of soft computing approaches in the interface analysis in a complex system and in the risk assessment of system integration readiness. The research aims to address some of the interface analysis challenges in a large-scale system development lifecycle such as the ones often experienced in aircraft development. The resulting product from this research is contributed to systems engineering by providing an easy-to-use interface assessment and methodology for a trained systems engineer to break the system into communities of dense interfaces and determine the integration readiness and risks based on those communities. As a proof of concept this methodology is applied on a power seat system in a commercial aircraft with data from the Critical Design Review”--Abstract, page iv

A Cluster-Based Framework for Interface Analysis in Large-Scale Aerospace Systems

This paper proposes a framework using a community maturity level metric to determine the integration readiness of interface elements in a particular network cluster. As a proof of concept this methodology is applied to an aircraft seating system to assess the readiness of complex interfaces before proceeding to full-scale production and systems integration. A multi-objective genetic algorithm, MOGA-Net, is coupled with the Newman-Girvan modularity metric as a clustering algorithm. This algorithm identifies system elements grouped by common interfaces, referred to as community clusters. The TRL and IRL values for these elements is then used to calculate an overall community maturity level. The achieved performance in these clusters is then compared to the target performance to determine overall maturity of the interfaces. This is compared to other system readiness metrics and interface readiness metrics as applied to the aircraft seating system and was found to be more consistent with subject matter expert evaluations during the critical design review. This gives a better representation of the true readiness of system interfaces before entering design reviews to reduce overall integration risks

SoS Explorer Application with Fuzzy-Genetic Algorithms to Assess an Enterprise Architecture -- A Healthcare Case Study

Kevin Dooley (1997), defined Complex Adaptive System (CAS) as a group of semi-autonomous agents who interact in interdependent ways to produce system-wide patterns, such that those patterns then influence behavior of the agents. A healthcare system is considered as a Complex Adaptive System of system (SoS) with agents composed of strategies, people, process, and technology. Healthcare systems are fragmented with independent systems and information. The enterprise architecture (EA) aims to address these fragmentations by creating boundaries around the business strategy and key performance attributes that drive integration across multiple systems of processes, people, and technology. This paper uses a SoS Explorer to select an optimal architecture that provide the necessary capabilities to meet key performance attributes (KPAs) in a dynamic, complex healthcare business environment. The SoS Explorer produced an optimal meta-architecture where all but two systems (disease and facility processes) participated with many of the systems having at least four interfaces. The healthcare meta-architecture produced in this study is not a solution to address the challenges of the healthcare enterprise architecture but provides insight on the areas - systems, capabilities, characteristics, and interfaces - to pay attention to where agility is an important attribute and not to be severely compromised