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Access Type

WSU Access

Date of Award

January 2011

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Industrial and Manufacturing Engineering

First Advisor

Ratna B. Chinnam

Second Advisor

Alper E. Murat

Abstract

Recent growth in the scale and complexity of products and technologies in the defense and other industries is challenging product development, realization, and sustainment costs. Uncontrolled costs and routine budget overruns are causing all parties involved to seek lean product development processes and treatment of reliability, availability, and maintainability of the system as a true "design parameter". To this effect, accurate estimation and management of the system reliability of a design during the "earliest stages" of new product development is not only critical for managing product development and manufacturing costs but also to control life cycle costs (LCC). In this regard, the overall objective of this research study is to develop an integrated framework for "design for reliability" (DFR) during upfront product development by treating reliability as a design parameter. The aim here is to develop the theory, methods, and tools necessary for: 1) accurate assessment of system reliability and availability and 2) optimization of the design to meet system reliability targets. In modeling the system reliability and availability, we aim to address the limitations of existing methods, in particular the Markov chains method and the Dynamic Bayesian Network approach, by incorporating a Continuous Time Bayesian Network framework for more effective modeling of sub-system/component interactions, dependencies, and various repair policies. We also propose a multi-object optimization scheme to aid the designer in obtaining optimal design(s) with respect to system reliability/availability targets and other system design requirements. In particular, the optimization scheme would entail optimal selection of sub-system and component alternatives. The theory, methods, and tools to be developed will be extensively tested and validated using simulation test-bed data and actual case studies from our industry partners.