Abstract

Although a large number of degradation models for industrial systems have been proposed by researchers over the past few decades, the modeling of impacts of maintenance actions has been mostly limited to single-component systems. Among multi-component models, past work either ignores the general impact of maintenance, or is limited to studying failure interactions. In this article, we propose a multivariate imperfect maintenance model that models impacts of maintenance actions across sub-systems while considering continual operation of the unit. Another feature of the proposed model is that the maintenance actions can have any degree of impact on the sub-systems. In other words, we propose a multivariate recurrent event model with stochastic dependence, and for this model we present a two-stage approach which makes estimation scalable, thus practical for large-scale industrial applications. We also derive expressions for the Fisher information so as to conduct asymptotic statistical tests for the maintenance impact parameters. We demonstrate the scalability through numerical studies, and derive insights by applying the model on real-world maintenance records obtained from oil rigs. In the online supplemental material, we provide the following: (i) sketch of proof for likelihood, (ii) convergence analysis, (iii) contamination analysis, and (iv) a set of R codes to implement the current method.

摘要

尽管在过去的几十年里，研究人员提出了大量工业系统退化模型，但维护行为影响的建模大多仅限于单组件系统。在多组件模型中，过去的工作要么忽略了维护的一般影响，要么仅限于研究故障相互作用。在本文中，我们提出了一种多元不完善维护模型，该模型在考虑单元的连续运行的同时，对跨子系统的维护操作的影响进行建模。所提出模型的另一个特点是维护操作可以对子系统产生任何程度的影响。换句话说，我们提出了一个具有随机依赖性的多元循环事件模型，对于这个模型，我们提出了一个两阶段的方法，使估计具有可扩展性，因此适用于大规模工业应用。我们还导出了 Fisher 信息的表达式，以便对维护影响​​参数进行渐近统计检验。我们通过数值研究证明了可扩展性，并通过将模型应用于从石油钻井平台获得的真实维护记录中获得见解。在在线补充材料中，我们提供了以下内容：(i) 似然证明草图，(ii) 收敛分析，(iii) 污染分析，以及 (iv) 一组用于实现当前方法的 R 代码。并通过将模型应用于从石油钻井平台获得的真实维护记录中获得见解。在在线补充材料中，我们提供了以下内容：(i) 似然证明草图，(ii) 收敛分析，(iii) 污染分析，以及 (iv) 一组用于实现当前方法的 R 代码。并通过将模型应用于从石油钻井平台获得的真实维护记录中获得见解。在在线补充材料中，我们提供了以下内容：(i) 似然证明草图，(ii) 收敛分析，(iii) 污染分析，以及 (iv) 一组用于实现当前方法的 R 代码。