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48.8 SUMMARY
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Our discussions of statistical in uences on system design practices were predicated on a basic understanding of statistical methods and provided a high-level overview of key statistical concepts that in uence SE design decisions. We highlighted the importance of using statistical methods to de ne acceptable or desirable design ranges for input and output data. We also addressed the importance of establishing boundary conditions for NORMAL operating ranges, CAUTIONARY ranges, WARNING ranges, as well as establishing safety margins. Using the basic concepts as a foundation, we addressed the concept of cumulative errors, circular error probabilities (CEP), and data correlation. We also addressed the need to bound acceptable or desirable system outputs that include products, by-products, and services. Statistical data variances have signi cant in uence on SE technical decisions such as system performance, budgets, and safety margins and operational and system effectiveness. What is important is that SEs: 1. Learn to recognize and appreciate engineering input/output data variances 2. Know WHEN and HOW to apply statistical methods to understand SYSTEM interactions with its OPERATING ENVIRONMENT.
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GENERAL EXERCISES
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1. Answer each of the What You Should Learn from This questions identi ed in the Introduction. 2. Refer to the list of systems identi ed in 2. Based on a selection from the preceding chapter s General Exercises or a new system, selection, apply your knowledge derived from this chapter s topical discussions. Speci cally identify the following: (a) What inputs of the system can be represented by statistical distributions (b) How would you translate those inputs into a set of input requirements (c) Based on processing of those inputs, do errors accumulate and, if so, what is the impact (d) How would you specify requirements to minimize the impacts of errors
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ORGANIZATIONAL CENTRIC EXERCISES
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1. Contact a technical program in your organization. Research how the program SEs accommodated statistical variability for the following: (a) Acceptable data input and output ranges for system processing (b) External data and timing variability
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48
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Statistical In uences on System Design
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2. For systems that require performance monitoring equipment such as gages, meters, audible warnings, and ashing lights, research how SEs determined threshold values for activating the noti cations or indications.
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REFERENCE
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Defense Systems Management College (DSMC). 1998. DSMC Test and Evaluation Management Guide, 3rd ed. Defense Acquisition Press. Ft. Belvoir, VA.
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ADDITIONAL READING
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Blanchard, Benjamin S., and J. Fabrycky, Wolter. 1990. Systems Engineering and Analysis, 2nd ed. Englewood Cliff, NJ: Prentice-Hall. Langford, John W. 1995. Logistics: Principles and Applications. New York: McGraw-Hill. National Aeronautics and Space Administration (NASA). 1994. Systems Engineering Toolbox for DesignOriented Engineers. NASA Reference Publication 1358. Washington, DC.
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System Performance Analysis, Budgets, and Safety Margins
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49.1 INTRODUCTION
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System effectiveness manifests itself via the cumulative performance results of the integrated set of System Elements at a speci c instance in time. That performance ultimately determines mission and system objectives success in some cases, survival. When SEs allocate system performance, there is a tendency to think of those requirements as static parameters for example, shall be +12.3 0.10 vdc. Aside from status switch settings or con guration parameters, seldom are parameters static or steady state. From an SE perspective, SEs partition and organize requirements via a hierarchical framework. Take the example of static weight. We have a budget of 100 pounds to allocate equally to three components. Static parameters make the SE requirements allocation task a lot easier. This is not the case for many system requirements. How do we establish values for system inputs that are subject to variations such as environmental conditions, time of day, time of year, signal properties, human error and other variables System requirement parameters are often characterized by statistical value distributions such as Normal (Gaussian), Binomial, and LogNormal (Poisson) with frequencies and tendencies about a mean value. Using our static requirements example above, we can state that the voltage must be constrained to a range of +12.20vdc (-3s) to +12.40 vdc (+3s) with a mean of +12.30 vdc for a prescribed set of operating conditions. On the surface, this sounds very simple and straightforward. The challenge is: How did SEs decide: 1. That the mean value needed to be +12.30 vdc 2. That the variations could not exceed 0.10 vdc This simple example illustrates one of the most challenging and perplexing aspects of System Engineering allocating dynamic parameters. Many times SEs simply do not have any precedent data. For example, consider human attempts to build bridges, develop and y an aircraft, launch rockets and missiles, and land on the Moon and Mars. Analysis with a lot of trial and error data collection and observation may be all you have to establish initial estimates of these parameters. There are a number of ways one can determine these values. Examples include:
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System Analysis, Design, and Development, by Charles S. Wasson Copyright 2006 by John Wiley & Sons, Inc.
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