Abstract

In this paper, we present a risk evaluation and maintenance strategy optimization approach for systems with parallel identical assets subject to continuous deterioration. System performance is defined by the number of functional assets, and the penalty cost is measured by the loss of performance. To overcome the practical challenges of information sparsity, we employ a Bayesian framework to dynamically update unknown parameters in a Wiener degradation model. Order statistics are utilized to describe the failure times of assets and the stepwise incurred performance penalty cost. Furthermore, based on the Bayesian parameter inferences, we propose a short-term value-based replacement policy to minimize the expected cost rate in the current planning horizon. The proposed strategy simultaneously considers the variability of parameter estimators and the inherent uncertainty of the stochastic degradation processes. A simulation study and a realistic example from the petrochemical industry are presented to demonstrate the proposed framework.

摘要

在本文中，我们提出了一种针对具有并行相同资产且不断恶化的系统的风险评估和维护策略优化方法。系统性能由功能资产的数量定义，罚款成本由性能损失衡量。为了克服信息稀疏性的实际挑战，我们采用贝叶斯框架来动态更新Wiener退化模型中的未知参数。订单统计用于描述资产的故障时间和逐步产生的性能损失成本。此外，基于贝叶斯参数推论，我们提出了一种基于价值的短期替换策略，以最大程度地减少当前计划范围内的预期成本率。提出的策略同时考虑了参数估计量的可变性和随机退化过程的固有不确定性。给出了石化行业的仿真研究和实际示例，以证明所提出的框架。