Date of Graduation

12-2021

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Engineering (PhD)

Degree Level

Graduate

Department

Industrial Engineering

Advisor/Mentor

Xiao Liu

Committee Member

Edward Pohl

Second Committee Member

Yili Hong

Third Committee Member

Shengfan Zhang

Keywords

Bayesian Statistics, Data Science, Machine Learning, Operations Research, Statistical Learning

Abstract

With the recent advances in sensor technology, it is much easier to collect and store streams of system operational and environmental (SOE) data. These data can be used as input to model the underlying behavior of complex engineered systems and phenomenons if appropriate algorithms with well-defined assumptions are developed. This dissertation is comprised of the research work to show the applicability of SOE data when fed into proposed tailored algorithms. The first purposes of these algorithms are to estimate and analyze the reliability of a system as elaborated in Chapter 2. This chapter provides the derivation of closed-form expressions that give the probability that a system fails due to a specific degradation process when degradation processes are independent dependent. It also explains how to model the degradation of each process when they subject the system to constant but varying rate failure. The second goal is to model the long-term behavior of the system state variables when these variables are affected by environmental and operational conditions 3. This chapter shows the underlying system variables follow a hidden Markov process and are possibly dependent on the covariance of the noise. In the last chapter, the author's purpose is to explore more data science applications and extend the scope of the research to areas other than reliability. In this regard, chapter 4 proposes a new approach to find the root causes of a public health issue, specifically, food-borne disease. To fulfill this goal, an appropriate statistical model based on the Bayesian viewpoint is developed and the probability of each distributed food product being contaminated is computed. The results show how the streaming SOE data when driving the statistical algorithms provide further insight into the underlying behavior of a system or causes of a phenomenon.

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