College of Computing, Engineering & Construction
Master of Science in Electrical Engineering (MSEE)
NACO controlled Corporate Body
University of North Florida. School of Engineering
Dr. O. Patrick Kreidl
Dr. Alan Harris
Dr. Thobias Sando
Dr. Brian T. Kopp
Dr. Osama M. Jadaan
Dr. William F. Klostermeyer
Under similar conditions, products that are designed and used for similar tasks fail similarly. Developers may become aware of various product failure modes during the initial stages of new product generation, where redesign and failure mitigation processes can occur with minimal detriment to consumer safety. Developers strive to mitigate the potential for catastrophic failures. This thesis concentrates on when these failures occur outside of controlled conditions, specifically where the development of processes feature low accuracy sensing techniques that impact the safety and operation of the end user. This thesis develops a set of statistical analysis simulation techniques using two existing methods: Sequential Analysis and Quickest Detection. Through the comparison of method-specific features, this thesis aims to assist future researchers unfamiliar with these methods to understand the individual characteristics of each as they pertain to failure mitigation. Each detection method is subjected to investigation via a pair of sensor models, a strong sensor and a weak sensor. Variable detection settings are used to quantify the operational characteristics of these sensors and their individual means of analysis. This thesis then compares both statistical techniques to recognize their overall usefulness to the topic of product failure analysis and mitigation pertaining to lower accuracy sensing processes that require longer sampling periods for better informed decisions. It is ascertained that the Sequential Analysis technique is best used when the initial system state is not yet known to the observer. The Quickest Detection method should be utilized when the initial state of a system is known and it is imperative to detect, with minimal delay, the occurrence of a random change-point in the operational status of the system.
Beachler, Christopher M., "Product Failure Recognition Via Comparison Of Sequential and Quickest Detection Algorithms" (2021). UNF Graduate Theses and Dissertations. 1022.