Redundancy is an effective, high-level solution to the requirement for reliable safety-critical systems, but it comes at the cost of Size, Weight and Power (SWaP) and reduced capability. A modeling and simulation framework was developed to address the need for robust design alternatives to redundancy. Robustness, in our application, is treated as the insensitivity of the performance with reference to specification. The necessity to characterize both reliability and robustness in the same framework has resulted in a time-domain simulation approach to modeling behaviors associated with unreliability and a lack of robustness. The incorporation of these features offers a novel insight into potential applications of prognostic technology. Further development of this approach has the potential to allow designers to choose how risks associated with failures are mitigated, by redundancy, robustness, or prognosis.
By modeling the life of parts, the factors that impact them and the resulting behaviors, the observability and predictability (even controllability in the case of optimized, fault-tolerant, closed-loop control) of faults and failures is identified. Designers can determine which parts of a system would benefit from prognostic health management (PHM) technologies, adaptive / tolerant features to yield robust design, or redundancy based approaches. The complex causality in the models requires a Monte Carlo approach analogous to the simulation of fleets of systems; this, combined with the ability to simulate systems made from new and old parts, can inform strategies for condition-based maintenance (CBM). We present the mathematical modeling concept and the simulation framework which permits comparative assessment of reliability, robustness and prognostics. The multi-hierarchical, systems integration aspects inherent to the concept make this technique highly applicable to real- world dynamic systems. The framework also supports statistical, standards based and physics-of-failure descriptions of stress, aging, fault and failure behaviors in a unified way. There are challenges to be overcome in realizing the benefits of this approach to model-based system design. Issues of model validation, data availability and computational burden are recognized and discussed. As we show, these challenges can be overcome to produce new design tools providing better products and transparent project quality.
How to Cite
prognostics, Model-Based Design, time-domain analysis, reliability, monte carlo simulation, Behavior modeling, Robustness
Zio, E., (2009) Reliability Engineering: Old Problems and New Challenges, Reliability Engineering and System Safety (94), pp. 125-141.
Siu, N., (1994) Risk Assessment for Dynamic Systems: An Overview, Reliability Engineering and System Safety (43), pp. 43-73.
Kuehl, R. W., (2010) Using the Arrhenius Equation to Predict Drift in Thin Film Resistors, CARTS Europe 2010 – 22nd Annual Passive Components Symposium, (pp 121-133), November 10-11, International Congress Center, Munich, Germany.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.