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Aplica??o do método de Monte Carlo para a previs?o de falhas: uma ferramenta de apoio à gest?o da manuten??o

机译:蒙特卡洛方法在故障预测中的应用:支持维护管理的工具

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The maintenance field has undergone important changes in the last decades, oriented, mainly, by the evolution of the managerial concepts in the companies. Although it has been treated for long periods as an onerous sector for organizations, maintenance is pointed out in the latest literature as a huge source of competitiveness. To explore its potential, however, Maintenance Management must incorporate Engineering as the driving force for routine processes and improvements. In a practical way, the company must work to avoid failures or, at least, to foresee them. In line with this strategic vision, the present work engages in the development and validation of a failure prediction system. Using the Monte Carlo Method, this article integrates a quantitative study of modeling and simulation. From mathematical and statistical concepts, different failure prediction series were formulated and comparative analyses were performed on their precisions. As results, it was verified the effectiveness of the method in determining the moment of occurrence of failures from numerical simulations and evidenced the optimal regions of prediction of each proposed series. Among the main contributions of the study, we highlight the higher precision of the series simulated by the Monte Carlo Method in relation to the series estimated from the historical average of the data, despite the good adjustment of these series in selected areas of the real curve. Future works will investigate the behavior of other models of a series of failures, generated from new combinations of the proposed parameters.
机译:在过去的几十年中,维护领域发生了重要的变化,主要是随着公司管理理念的发展而变化。尽管长期以来一直将其视为组织的繁重部门,但最新文献中指出维护是竞争力的巨大来源。但是,为了探索其潜力,维护管理必须将工程技术作为常规流程和改进的驱动力。公司必须以实际的方式努力避免失败,或者至少可以预见到失败。根据这一战略远景,本工作致力于故障预测系统的开发和验证。使用蒙特卡洛方法,本文整合了建模和仿真的定量研究。从数学和统计概念出发,制定了不同的故障预测序列,并对其精度进行了比较分析。结果,通过数值模拟验证了该方法在确定故障发生时刻的有效性,并证明了每个拟议系列的最佳预测区域。在这项研究的主要贡献中,尽管在实际曲线的选定区域中对这些序列进行了很好的调整,但我们强调了蒙特卡洛方法模拟的序列相对于根据数据的历史平均值估算的序列具有更高的精度。 。未来的工作将研究由建议参数的新组合产生的一系列故障的其他模型的行为。

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