ECE Oral Comprehensive Exam for Doctoral Candidacy By: Saikath Bhattacharya
When: Thursday,
December 17, 2020
10:30 AM
-
12:30 PM
Where: > See description for location
Cost: Free
Description: Topic: Tradespace Analysis Incorporating Reliability, Availability, Maintainability, and Affordability for Systems
Zoom Teleconference: https://umassd.zoom.us/j/99664514806
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 detailed consideration of tradeoffs, which should be agreed upon by stakeholders prior to committing to a particular design. However, explicit consideration of quantitative -ilities", 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 proposal presents two contributions (i) a method to incorporate reliability engineering into tradespace exploration, and (ii) a measure based on concepts from maintenance theory to provide a framework for the objective assessment of existing and future degradation models.
NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
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, Assistant Professor, Industrial, Manufacturing & Systems Engineering, Texas Tech University
*For further information, please contact Dr. Lance Fiondella at 508.999.8596, or via email at lfiondella@umassd.edu.
Zoom Teleconference: https://umassd.zoom.us/j/99664514806
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 detailed consideration of tradeoffs, which should be agreed upon by stakeholders prior to committing to a particular design. However, explicit consideration of quantitative -ilities", 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 proposal presents two contributions (i) a method to incorporate reliability engineering into tradespace exploration, and (ii) a measure based on concepts from maintenance theory to provide a framework for the objective assessment of existing and future degradation models.
NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
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, Assistant Professor, Industrial, Manufacturing & Systems Engineering, Texas Tech University
*For further information, please contact Dr. Lance Fiondella at 508.999.8596, or via email at lfiondella@umassd.edu.
Contact:
ECE: Electrical & Computer Engineering Department 508.999.9164 http://www.umassd.edu/engineering/ece/
Topical Areas: General Public, University Community, College of Engineering, Electrical and Computer Engineering