University Events

Description: Topic: Tradespace Analysis Incorporating Reliability, Availability, Maintainability, and Affordability

Zoom Teleconference: https://umassd.zoom.us/j/98896460978

Abstract:
Tradespace Exploration (TSE) is a process of analyzing various suitable alternatives in an intuitive manner. In recent years, TSE has emerged as a systematic strategy to assess the effectiveness and suitability of alternative conceptual designs. An advantage of TSE is that it promotes a detailed consideration of tradeoffs, which should be agreed upon by stakeholders prior to committing to a particular design. However, explicit consideration of the abilities, such as reliability, availability, maintainability, as well as their associated cost are often omitted from such studies.

A related trend in reliability engineering is the application of machine learning methods to various mission critical systems. Specifically, effective monitoring of a system's health can identify preventive maintenance actions based on a usage profile. In this regard, prognostics and health management (PHM) has emerged as a promising approach to predict the deterioration of components prior to failure so that they can be maintained on an as-needed basis instead of performing costly part replacement at regular intervals to preserve reliability and safety. Previous PHM studies emphasize degradation modeling and predictive algorithms to improve state of health predictions. However, most of these techniques focus on improving performance within a single maintenance cycle, while fewer studies consider the effectiveness of alternative degradation models and predictive algorithms over multiple successive maintenance intervals.

This dissertation presents contributions across two primary dimensions: (i) models incorporating reliability engineering into tradespace exploration and (ii) data-driven methods to assess PHM based on concepts from maintenance theory in order to provide a framework for the objective tradeoff assessment of existing and future models and algorithms.

Note: All ECE Graduate Students are ENCOURAGED to join the zoom teleconference. All interested parties are invited to join.

Advisor: Dr. Lance Fiondella
Committee Members: Dr. Honggang Wang, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Ming Shao, Department of Computer and Information Science, UMASS Dartmouth; Dr. Yisha Xiang, Industrial, Manufacturing & Systems Engineering, Texas Tech University

*For further information, please contact Dr. Lance Fiondella via email at lfiondella@umassd.edu.