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ARA and ARI imperfect repair models: Estimation, goodness-of-fit and reliability prediction

机译:ARA和ARI不完善的维修模型:估计,拟合优度和可靠性预测

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An appropriate maintenance policy is essential to reduce expenses and risks related to equipment failures. A fundamental aspect to be considered when specifying such policies is to be able to predict the reliability of the systems under study, based on a well fitted model. In this paper, the classes of models Arithmetic Reduction of Age and Arithmetic Reduction of Intensity are explored. Likelihood functions for such models are derived, and a graphical method is proposed for model selection. A real data set involving failures in trucks used by a Brazilian mining is analyzed considering models with different memories. Parameters, namely, shape and scale for Power Law Process, and the efficiency of repair were estimated for the best fitted model. Estimation of model parameters allowed us to derive reliability estimators to predict the behavior of the failure process. These results are a valuable information for the mining company and can be used to support decision making regarding preventive maintenance policy. (C) 2015 Elsevier Ltd. All rights, reserved.
机译:适当的维护策略对于减少与设备故障相关的费用和风险至关重要。指定此类策略时要考虑的一个基本方面是能够基于一个很好拟合的模型来预测所研究系统的可靠性。本文探讨了年龄的算术降低和强度的算术降低模型的类别。推导了此类模型的似然函数,并提出了用于模型选择的图形方法。考虑到具有不同内存的模型,分析了涉及巴西采矿公司使用的卡车故障的真实数据集。对于最佳拟合模型,估计了参数(即幂律过程的形状和比例以及修复效率)。模型参数的估计使我们能够导出可靠性估计器,以预测故障过程的行为。这些结果对于矿业公司而言是宝贵的信息,可用于支持有关预防性维护政策的决策。 (C)2015 Elsevier Ltd.保留所有权利。

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