DATAWorks Sessions Archive

Statistical engineering has been defined as: “The study of how to best utilize statistical concepts, methods, and tool, and integrate them with IT and other relevant sciences to generate improved results.” A key principle is that significant untapped benefits are often achievable by integrating multiple methods in novel ways to address a problem, without having to invent new statistical techniques. In this presentation, we discuss the application of statistical engineering to the problem of design and analysis of mixture experiments when process variables are also involved. In such cases, models incorporating interaction between the mixture and process variables have been developed, but tend to require large designs and models. By considering models nonlinear in the parameters, also well known in the literature, we demonstrate how experimenters can utilize an alternative, iterative strategy in attacking such problems. We show that this strategy potentially saves considerable experimental time and effort, while producing models that are nearly as accurate as much larger linear models. These results are illustrated using two published data sets and one new data set, all involving interactive mixture and process variable problems.

Co-authors: Dr. Darryl Ahner, Director, STAT COE Dr. Lenny Truett, STAT COE Mr. Scott Wacker, 96 TG/OL-ACS. This presentation will present the benefits of sequential testing and demonstrate how sequential testing can be used to address complex test conditions by developing well controlled early experiments to explore basic questions before proceeding to full-scale testing. This approach can result in increased knowledge and decreased cost. As of FY13 the Air Force had spent an estimated $47M on dry bay fire testing making fire the largest cost contributor for Live Fire Test and Evaluation (LFT&E) programs. There is currently an estimated 60% uncertainty in total platform vulnerable area (Av) driven by probability of kill (PK) due to ballistically ignited fires. A large part of this uncertain comes from the fact that current spurt modeling does not predict fuel spurt delay with reasonable accuracy despite a large amount of test data. A low-cost sequential approach was developed to improve spurts models. Initial testing used a spherical projectile to test 10 different factors in a definitive screening design. Once the list of factors was refined, a second phase of testing determined if a suitable methodology could be developed to scaled results using water as a surrogate for JP-8 fuel. Finally testing was performed with cubical projectiles to evaluate the effect of fragment orientation. The entire cost for this effort was less than one or two typical full-scale live fire tests.

Two responses to an expensive, time consuming test on a final product will be referred to as “adhesion” and “strength”. A screening test was performed on compounds that comprise the final product. These screening tests are multivariate profile measurements. Previous models to predict the expensive, time consuming test lacked accuracy and precision. Data visualization was used to guide a statistical engineering model that makes use of multiple statistical techniques. The modeling approach raised some interesting statistical questions for partial least square models regarding over-fitting and cross validation. Ultimately, the model interpretation and the visualization both make engineering sense and led to interesting insights regarding the product development program and screening compounds.

Co-Authors: Sean A. Commo, Ph.D., P.E. and Peter A. Parker, Ph.D., P.E. NASA Langley Research Center, Hampton, Virginia, USA Austin D. Overmeyer, Philip E. Tanner, and Preston B. Martin, Ph.D. U.S. Army Research, Development, and Engineering Command, Hampton, Virginia, USA. The application of statistical engineering to helicopter wind-tunnel testing was explored during two powered rotor entries. The U.S. Army Aviation Development Directorate Joint Research Program Office and the NASA Revolutionary Vertical Lift Project performed these tests jointly at the NASA Langley Research Center. Both entries were conducted in the 14- by 22-Foot Subsonic Tunnel with a small segment of the overall tests devoted to developing case studies of a statistical engineering approach. Data collected during each entry were used to estimate response surface models characterizing vehicle performance, a novel contribution of statistical engineering applied to powered rotor-wing testing. Additionally, a 16- to 47-times reduction in the number of data points required was estimated when comparing a statistically-engineered approach to a conventional one-factor-at-a-time approach.

Co-Authors: Sean A. Commo, Ph.D., P.E. and Peter A. Parker, Ph.D., P.E. NASA Langley Research Center, Hampton, Virginia, USA Austin D. Overmeyer, Philip E. Tanner, and Preston B. Martin, Ph.D. U.S. Army Research, Development, and Engineering Command, Hampton, Virginia, USA. The application of statistical engineering to helicopter wind-tunnel testing was explored during two powered rotor entries. The U.S. Army Aviation Development Directorate Joint Research Program Office and the NASA Revolutionary Vertical Lift Project performed these tests jointly at the NASA Langley Research Center. Both entries were conducted in the 14- by 22-Foot Subsonic Tunnel with a small segment of the overall tests devoted to developing case studies of a statistical engineering approach. Data collected during each entry were used to estimate response surface models characterizing vehicle performance, a novel contribution of statistical engineering applied to powered rotor-wing testing. Additionally, a 16- to 47-times reduction in the number of data points required was estimated when comparing a statistically-engineered approach to a conventional one-factor-at-a-time approach.

In this case study we demonstrate an application of Pratt & Whitney’s “Design for Variation” discipline applied to the task of a roller bearing design. The ultimate goal, in this application, was to utilize test data from the “real world” to calibrate a computer model used for design and ensure that roller bearing designs obtained from this model were optimized for maximum robustness to major sources of variation in bearing manufacture and operation. The “Design for Variation” process provides engineers with many useful analysis results even before real world data is applied: high fidelity sensitivity analysis, uncertainty analysis (quantifying a baseline risk of failing to meet design intent) and model verification. The combining of real world data and Bayesian statistical methods that Design for Variation employs to calibrate models, however, goes a step further, validating the accuracy of the model’s outputs and quantifying any bias between the model and the real world. As a result of this application, the designers were able to identify the sources of the bias and correct the model’s physics-based aspects to more accurately model reality. The improved model is now integrated into all successive bearing design activities. The benefits of this method, which only required a small amount of high quality test data, are now available to all present and future roller bearing designs.

Co-authors: Adam L. Pintar, Blaza Toman, and Dennis Leber. A task of the Domestic Nuclear Detection Office (DNDO) is the evaluation of radiation and nuclear (rad/nuc) detection systems used to detect and identify illicit rad/nuc materials. To obtain estimated system performance measures, such as probability of detection, and to determine system acceptability, the DNDO sometimes conduct large scale field tests of these systems at great cost. Typically, non adaptive designs are employed where each rad/nuc test source is presented to each system under test a predetermined and fixed number of times. This approach can lead to unnecessary cost if the system is clearly acceptable or unacceptable. In this presentation, an adaptive design with Bayesian decision theoretic foundations is discussed as an alternative to, and contrasted with, the more common single stage design. Although the basis of the method is Bayesian decision theory, designs may be tuned to have desirable type I and II error rates. While the focus of the presentation is a specific DNDO example, the method is applicable widely. Further, since constructing the designs is somewhat compute intensive, software in the form of an R package will be shown and is available upon request.

Modeling and simulation (M&S) is often an important element of operational evaluations of effectiveness, suitability, survivability, and lethality. For example, the testing of new systems designed to operate against advanced foreign threats, as well as the testing of systems of systems, will involve the use of M&S to examine scenarios that cannot be created using live testing. In order to have an adequate understanding of, and confidence in, the results obtained from M&S, statistically rigorous techniques should be applied to the validation process wherever possible. Design of experiments methodologies should be employed to determine what live and simulation data are needed to support rigorous validation, and formal statistical tests should be used to compare live and simulated data. This talk will discuss the importance of M&S in operational testing through a few examples, and outline several statistically rigorous techniques for validation.

The key to acquiring a cybersecure system is the ability to drive considerations about security from the operational level into the tactics and procedures. For T&E to support development and acquisition decisions, we must also adopt the perspective that a cyberattack is an attack on the mission using technology. A well-defined process model linking tools, tasks, and operators to mission performance supports this perspective. We will discuss an approach based on best practices learned from various DHS programs.

There are many testing situations that historically involve a large number of runs. The use of experimental design methods can reduce the number of runs required to obtain the information desired. Example applications include wind tunnel test campaigns, computational experiments and live fire tests. In this work we present three case studies conducted under the auspices of the Science of Test Research Consortium comparing the information obtained via a historical experimental approach with the information obtained via an experimental design approach. The first case study involves a large scale wind tunnel experimental campaign. The second involves a computational fluid dynamics model of a missile through various speeds and angles of attack. The third case involves ongoing live fire testing involve hot surface testing. In each case, results suggest a tremendous opportunity to reduce experimental test efforts without losing test information.