Date of Graduation

5-2024

Document Type

Thesis

Degree Name

Bachelor of Science in Data Science

Degree Level

Undergraduate

Department

Data Science

Advisor/Mentor

Sullivan, Kelly

Committee Member/Reader

Liao, Haitao

Committee Member/Second Reader

Pohl, Edward

Abstract

This technical report details an innovative approach in reliability engineering aimed at maximizing system durability through a synergistic use of physical experimentation and computer-based modeling. Our methodology explores the efficient design and analysis of computer experiments and physical tests to facilitate accelerated reliability growth, while leveraging a sequential integration of data from these two distinct sources: costly physical experiments, characterized by random errors, and inexpensive computer simulations, marked by inherent systematic errors. The key innovation lies in the adoption of a closed-loop design and analysis method. This method begins by identifying a viable subset of important environmental stressors—such as temperature, wind speed, precipitation, and payload—for physical tests, along with the complementary design variables for computer experimentation. By employing this strategic sequential integration of physical and simulated data, we make efficient use of a limited dataset, consisting of a small number of physical observations and a relatively larger set of computer model simulations. The subsequent analysis of this mixed data source enables the development of a curve optimization model using statistical and machine learning methods. This model guides the allocation of experimental resources, iterating between computer experiments and physical tests until the function value converges to the target.

Keywords

sequential, test planning, reliability, optimization, hybrid, stressor

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