Event
Meng Xu Thesis Defense
Monday, July 8, 2019
2:00 p.m.-4:00 p.m.
2164 Glenn L. Martin Hall
Title: Using the Q-Weibull Distribution for Reliability Engineering Modeling and Applications
Committee:
Jeffrey W. Herrmann, Advisor and Chair
Enrique López Droguett, Co-Advisor
Mohammad Modarres
Katrina Groth
Mohamad Al-Sheikhly, Dean’s Representative
Abstract:
Modeling and improving system reliability require selecting appropriate probability distributions for describing the uncertainty in failure times. The q-Weibull distribution, which is based on the Tsallis non-extensive entropy, is a generalization of the Weibull distribution in the context of non-extensive statistical mechanics. The q-Weibull distribution can be used to describe complex systems with long-range interactions and long-term memory, can model various behaviors of the hazard rate, including unimodal, bathtub-shaped, monotonic, and constant, and can reproduce both short and long tailed distributions. Despite its flexibility, the q-Weibull has not been widely used in reliability applications partly because parameter estimation is challenging. This research will develop and test an adaptive hybrid artificial bee colony approach for estimating the parameters of a q-Weibull distribution. This research will also demonstrate that the q-Weibull distribution has a superior performance over Weibull distribution in the characterization of lifetime data with non-monotonic hazard rate. Moreover, in terms of system reliability, the q-Weibull distribution can model systems with dependence. This research will explore the connection of q-Weibull distribution with Clayton copula in the context of a series system with a minimal repair process and multiple dependent components and determine how well the q-Weibull distribution predicts system failures using data that is characterized by a bathtub-shaped intensity function.
Meng Xu has applied these models to analyze failure data for electrical motors, large generators, and load-haul-dump machines used in mining.
