Showing papers on "Technical performance measure published in 2018"

A model-based systems engineering approach for technical measurement with application to a CubeSat

Abstract: While much has been written about technical measurement and Model-Based Systems Engineering (MBSE), very little literature exists that ties the two together. What does exist treats the topic in a general manner and is void of details. Given the vital role that technical measurement plays in the systems engineering process, and the ever increasing adoption of the MBSE approach, there is a growing need to define how technical measurement would be implemented as part of a MBSE approach. The purpose of this paper is to address that need. Technical measurement is defined as the set of measurement activities used to provide insight into the progress made in the definition and development of the technical solution and the associated risks and issues [1]. Technical measures are used to: determine if the technical solution will meet stakeholder needs, provide early indications if the development effort is not progressing as needed to meet key milestones, predict the likelihood of the delivered solution to meet performance requirements, monitor high risk items, and assess the effectiveness of risk mitigation actions. MBSE is defined as the formalized application of modeling to support system requirements, design, analysis, verification, and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phases [2]. The benefits of using an MBSE approach over a traditional document-based systems engineering approach are: enhanced communications, reduced development risk, improved quality, and enhanced knowledge transfer. This paper defines a MBSE approach for technical measurement that begins with a set of mission objectives derived from stakeholder concerns. The objectives and concerns are represented as elements captured in the system model. Next, Measures of Effectiveness (MOEs) are derived from the mission objectives. Initially, these MOEs are captured in a special model element that allows for the MOEs to be described in a natural language format that stakeholders will understand. Those initial MOEs are then quantified and captured as value properties of the Enterprise block. The MOEs are traced back to their originating source in the mission objectives. Next, Measures of Performance (MOPs) are derived from the enterprise-level MOEs and captured as value properties of the System block. The derivation of the MOPs is captured through the development of constraint blocks and parametric diagrams. This provides for traceability between MOPs and MOEs and supports performance analysis of the MOPs to predict if the MOEs will be met. MOPs are also traced to system requirements captured in the system model. Next, the process steps at the system-level are repeated at the subsystem-level to derive Technical Performance Measures (TPMs). These TPMs are traced back to MOPs and subsystem requirements in the same manner as described for MOPs. Examples are provided throughout the paper which illustrate the application of this approach to a CubeSat. Using a CubeSat example is appropriate given the historically high failure rate and rapid growth of these missions and the role technical measurement could play in increasing their success.

Creating comparable public tolerance and technical performance measures for critical infrastructure resilience evaluation

Abstract: No consensus currently exists on how to measure and evaluate Critical Infrastructure (CI) resilience. Attempting to use the public’s declared coping capacity as a target for CI resilience, this paper explores how to develop relevant resilience performance measurements that enable comparison to the tolerance levels of the general public. To do so, one must first establish the normal performance of the system and the applicable performance measures. Then, a survey is used to convert public perception into these measures as to enable comparison with the technical resilience performance. The CI resilience will be presented through a family of so-called resilience triangles which will illustrate the evolution of the performance, before, during and after a crisis event. A case study of the Municipal Water Network of Barreiro, Portugal, is used. The overall performance is preferably described with the categories quality, quantity and delivery. In quantifying the performance the importance of what is being assessed, to what hazard and for which end-user became evident.