Dependability-Based Design Optimization of Degrading Engineering Systems

In this paper, we present a methodology that helps select the distribution parameters in degrading multiresponse systems to improve dependability at the lowest lifetime cost. The dependability measures include both quality (soft failures) and reliability (hard failures). Associated costs of scrap, rework, and warrantee work are included. The key to the approach is the fast and efficient creation of the system cumulative distribution function through a series of time-variant limit-state functions. Probabilities are evaluated by Monte Carlo simulation although the first-order reliability method is a viable alternative. The cost objective function that is common in reliability-based design optimization is expanded to include a lifetime loss of performance cost, herein based on present worth theory (also called present value theory). An optimum design in terms of distribution parameters of the design variables is found via a methodology that involves minimizing cost under performance policy constraints over the lifetime as the system degrades. A case study of an over-run clutch provides the insights and potential of the proposed methodology.