Abstract
A major hurdle in the application of reliability-based design optimization (RBDO) to time-dependent systems is the continual interplay between calculating time-variant reliability (to ensure reliability policies are met) and moving the design point to optimize some objective function, such as cost, weight, size and so forth. In most cases the reliability can be obtained readily using so-called fast integration methods. However, this option is not available when certain stochastic processes are invoked to model gradual damage or deterioration. In this case, sampling methods must be used. This paper provides a novel way to obviate this inefficiency. First, a meta-model is built to relate time-variant system reliability to the entire design space (and noise space if required). A design of experiments paradigm and Monte Carlo simulation using the mechanistic model determines the corresponding system reliability accurately. A moving least-squares meta-model relates the data. Then, the optimization process to find the best design point, accesses the meta-model to quickly evaluate objectives and reliability constraints. Case-studies include a parallel Daniel's system and a series servo control system. The meta-model approach is simple, accurate and very fast, suggesting an attractive means for RBDO of time-dependent systems.
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The research was supported by a grant from 2018 Subsidy for overseas dispatch research of Andong National University.
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Recommended by Associate Editor Byeng Dong Youn
Young Kap Son received his Ph.D. from the Department of Systems Design Engineering in 2006 at the University of Waterloo in Canada. Currently he is a Professor in Mechanical & Automotive Engineering at the Andong National University, Korea. His current research interests include economic-based design of uncertain dynamic systems for reliability improvement, time-variant reliability estimation, and physics of failure.
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Savage, G.J., Son, Y.K. Reliability-based design optimization of time-dependent systems with stochastic degradation. J Mech Sci Technol 33, 5963–5977 (2019). https://doi.org/10.1007/s12206-019-1141-0
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DOI: https://doi.org/10.1007/s12206-019-1141-0