Collaborative Research: Automated Knowledge Discovery in Reliability and Healthcare from Complex Data with Covariates

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Collecting and analyzing data with covariates such as temperature, humidity, and radiation level are everyday activities in science and engineering. An important example is accelerated life testing data used in the design of products such as lithium-ion batteries. Such data are collected by exposing test units to harsher-than-normal conditions to expedite the failure process. The resulting failure times are modeled by a probability distribution and a life-stress relationship. However, if the probability distribution and/or the life-stress relationship selected cannot adequately describe the underlying failure process, the resulting reliability prediction may be misleading. A similar example is also encountered in healthcare systems, where it is crucial to quantify probability distributions of important measures such as the length-of-stay, waiting time, and disease progression, and the effects of influential covariates on these measures. This award supports fundamental research on automated knowledge discovery from complex data in reliability and healthcare with potential impacts in the areas of manufacturing, healthcare, energy, transportation, and aerospace industries. The research team will strive to broaden participation of underrepresented groups and minorities, and positively impact engineering education. The objective of this project is to investigate a new methodology for automated knowledge discovery from complex data with covariates using matrix-analytic models. Statistical tools and optimization algorithms will be developed for efficiently collecting such data or selecting the useful subsets from massive data for quick implementation. The research findings will help create a new avenue for modeling and interpreting such data in situations in which the data-generating mechanisms are unknown or difficult to analyze using existing statistical tools. To this end, an automated modeling methodology to construct general phase-type distributions incorporating covariates will be explored via mathematical optimization. To improve the statistical efficiency of data collection, an optimal experimental design methodology will be investigated, and viable computational tools for planning accelerated testing experiment with phase-type models will be studied. In addition, a data-selection approach based on the optimal experimental design methodology will be developed to maximize the utilization of healthcare data. The research findings will be validated by conducting accelerated tests of lithium-ion battery in the laboratory and collaborating with biomedical informatics services on targeted healthcare applications.