A new Bayesian model averaging ensemble modeling method for surrogate-assisted reliability-based design optimization of FBTAM
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Surrogate model
Bayesian Optimization
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This paper presents reliability assessment with incomplete service data on the example of impact wrenches. The significance and rules for determining reliability are presented. Examples of tool destruction during extreme operating conditions and during improper use are presented. The authors propose a model of reliability and described typical distributions. Then, an example of a test to evaluate reliability of a fully functional tool was presented. Finally, the analysis of the reliability of a wrench and its elements was presented.
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One of the main objectives of electric power utilities keeping up a continuous and adequate power supply to the customers at a sensible cost. This paper contributes to the solution of the reliability and quality assessment problems in power systems, using the (N-2) outage contingency scenario to evaluate power system’s reliability and quality levels. Therefore, the methodology presented in this paper is based on the integration of reliability measures, quality indices, and contingency analysis. While reliability formulas have traditionally been applied to small and illustrative power systems, large-scale reliability and quality assessment go far beyond direct implementation of formulas. Systems with hundreds of buses and tens of complex stations can only be analyzed using advanced and numerically effective large-scale algorithms for reliability and quality assessment as demonstrated in this paper.
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This paper proposes a reliability index considering the impact on power gird made by the HVDC transmission system. The application of traditional reliability index of energy availability appears problems and defects gradually. By improving the energy availability from the basic algorithm, the index of operation reliability is designed to evaluate the reliability contribution to overall system, which made by HVDC transmission system. The paper interprets the application of operation reliability by cases, in which the practical HVDC systems in China Southern Power Grid are presented. The result is helpful to provide some reference information on improving the reliability of the HVDC system and making decision from the perspective of optimizing maintenance arrangements.
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A new optimization framework for a high-dynamic point-to-point direct drive motion control system (HDPDMS) is proposed. The conventional system optimization approach considers all design parameters simultaneously, resulting in a high-dimensional search space and extensive computation. In contrast, the proposed framework uses a new DDM surrogate model that establishes a correlation between the key DDM characteristic parameters to decouple the whole optimization process. It begins with a system-level optimization to identify suitable driver types, motion profile design parameters, and characteristic parameters of the direct drive motors (DDMs) by the new surrogate model. Bayesian optimization then determines the DDM design parameters corresponding to the identified characteristic parameters. Once the DDM surrogate model is built, the proposed framework achieved the desired HDPDMS design in just 1 hour, saving 98.6% of computation time compared to the traditional approach. Additionally, multi-objective optimization and Gaussian process regression prediction intervals were employed to obtain a suitable training dataset and input range for the surrogate model, resulting in a 99.8% reduction in computation resources compared to the traditional DDM surrogate model. Through completing three unique motion task optimizations and creating a prototype, the optimization framework was proven effective, demonstrating the potential of this novel method.
Surrogate model
Bayesian Optimization
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In this paper, repairable system reliability assessment bases on operation reliability and performance reliability, and thus makes it possible to accurately evaluate the reliability status in the process of operation. This paper applies failure statistics to the operation reliability in different stages of maintenance and establishes reliability degradation model for measurable parameters. The paper considers diesel fuel injection system which is used in the study of reliability comprehensive evaluation, and the results show that the method proposed here possesses higher application value in the reliability evaluation.
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This Paper enlightens the significance of the reliability evaluation for an electrical power distribution network using the analytical technique FMEA. The power distribution system is subject to interruptions frequently as a lot of devices are responsible for its effective operation. All the possible failures of each component are considered and the reliability is evaluated in terms of system reliability indices like SAIFI, SAIDI, ENS, and ASAI. FMEA method observes the failure modes of a procedure and reduces it by ranking over its impacts. In this paper, RBTS bus2 distribution network is used for the analysis. The influences of various feeder reconfigurations are considered and the system reliability indices are obtained. The obtained results show that the reliability of the distribution system is enriched with various feeder reconfigurations. Reliability Evaluation helps to design the future Distribution system and its expansion.
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Bayesian optimization has become a fundamental global optimization algorithm in many problems where sample efficiency is of paramount importance. Recently, there has been proposed a large number of new applications in fields such as robotics, machine learning, experimental design, simulation, etc. In this paper, we focus on several problems that appear in robotics and autonomous systems: algorithm tuning, automatic control and intelligent design. All those problems can be mapped to global optimization problems. However, they become hard optimization problems. Bayesian optimization internally uses a probabilistic surrogate model (e.g.: Gaussian process) to learn from the process and reduce the number of samples required. In order to generalize to unknown functions in a black-box fashion, the common assumption is that the underlying function can be modeled with a stationary process. Nonstationary Gaussian process regression cannot generalize easily and it typically requires prior knowledge of the function. Some works have designed techniques to generalize Bayesian optimization to nonstationary functions in an indirect way, but using techniques originally designed for regression, where the objective is to improve the quality of the surrogate model everywhere. Instead optimization should focus on improving the surrogate model near the optimum. In this paper, we present a novel kernel function specially designed for Bayesian optimization, that allows nonstationary behavior of the surrogate model in an adaptive local region. In our experiments, we found that this new kernel results in an improved local search (exploitation), without penalizing the global search (exploration). We provide results in well-known benchmarks and real applications. The new method outperforms the state of the art in Bayesian optimization both in stationary and nonstationary problems.
Bayesian Optimization
Surrogate model
Global Optimization
Kernel (algebra)
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