System Reliability Based Design Optimization with Monte Carlo simulation

Wang, Haikun; Gong, Zhean; Huang, Hong‐Zhong; Zhang, Xiaoling; Lv, Zhiqiang

doi:10.1109/icqr2mse.2012.6246423

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…17 (2) Reliability allocation or design optimization, which is usually performed before new product launches on the market by balancing cost and quality. 18,19 However, these optimizations are focused more on the viewpoint of system designers rather than that of users. Users are generally more concerned with maintaining the reliability of an operating system at a high level, especially in the event of component degradation or failure.…”

Section: Introductionmentioning

confidence: 99%

System reliability evaluation and dynamic optimization based on an improved reliability block diagram

Tianyu,

Zhengqiang,

Guopeng

2023

Proceedings of the Institution of Mechanical Engineers, Part O:

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Reliability block diagram (RBD) is an effective tool for modeling and evaluating system reliability. During operation, a system’s reliability may decrease significantly due to the failure of certain critical nodes and thus should be reconfigured. This paper presents a framework for system reliability evaluation and dynamic optimization based on RBD, designed from the perspective of system users. First, we improve the classic RBD model with a new encoding scheme and develop an accurate RBD computation algorithm that is easily recognized by computers and highly efficient. Second, we create an optimization algorithm based on Tabu Search to reconfigure the system after node failure, striking a balance between system reliability recovery and RBD variation amplitude. Finally, we provide some numerical examples and a computational experiment based on a practical instance from a navy fleet to demonstrate the correctness and effectiveness of our proposed methods.

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Section: Introductionmentioning

confidence: 99%

System reliability evaluation and dynamic optimization based on an improved reliability block diagram

Tianyu,

Zhengqiang,

Guopeng

2023

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…In [24], Kriging surrogate models assist a worst-case robustness scenario for the optimization of electromagnetic devices. Simulation-based robust design strategies were developed by employing the Monte Carlo (MC) [25] sampling method to investigate the impact of parameter variations on product performance [26,27]. Most of these approaches, however, rely on methods that are not data-efficient.…”

Section: Introductionmentioning

confidence: 99%

A Robust Bayesian Optimization Framework for Microwave Circuit Design under Uncertainty

et al. 2022

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In modern electronics, there are many inevitable uncertainties and variations of design parameters that have a profound effect on the performance of a device. These are, among others, induced by manufacturing tolerances, assembling inaccuracies, material diversities, machining errors, etc. This prompts wide interests in enhanced optimization algorithms that take the effect of these uncertainty sources into account and that are able to find robust designs, i.e., designs that are insensitive to the uncertainties early in the design cycle. In this work, a novel machine learning-based optimization framework that accounts for uncertainty of the design parameters is presented. This is achieved by using a modified version of the expected improvement criterion. Moreover, a data-efficient Bayesian Optimization framework is leveraged to limit the number of simulations required to find a robust design solution. Two suitable application examples validate that the robustness is significantly improved compared to standard design methods.

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“…In this case, size of MCS, realization of random variables and accepted failure probability are used to approximate the probabilistic constraint [19,22].…”

Section: Two-level Approachesmentioning

confidence: 99%

Solving reliability analysis problems in the polar space

Ezzati

Mammadov

Kulkarni

2014

International Journal of Applied Mathematical Research

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An optimization model that is widely used in engineering problems is Reliability-Based Design Optimization (RBDO). Input data of the RBDO is non-deterministic and constraints are probabilistic. The RBDO aims at minimizing cost ensuring that reliability is at least an accepted level. Reliability analysis is an important step in two-level RBDO approaches. Although many methods have been introduced to apply in reliability analysis loop of the RBDO, there are still many drawbacks in their efficiencies and stabilities. This paper proposes a novel method that converts the constrained reliability analysis problem to an unconstrained minimization problem in order to improve efficiency of the solution. Number of numerical experiments is conducted and then performance of the proposed method is compared with the existing reliability analysis methods.

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System Reliability Based Design Optimization with Monte Carlo simulation

Cited by 5 publications

References 10 publications

System reliability evaluation and dynamic optimization based on an improved reliability block diagram

System reliability evaluation and dynamic optimization based on an improved reliability block diagram

A Robust Bayesian Optimization Framework for Microwave Circuit Design under Uncertainty

Solving reliability analysis problems in the polar space

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