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Optimal maintenance decisions for deteriorating quoin blocks in miter gates subject to uncertainty in the condition rating protocol

机译:用于在条件评级协议中对斜闸闸门中的Quoin块进行Quoin块的最佳维护决策

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摘要

Condition assessments and rating systems are frequently used by field engineers to assess inland navigation assets and components. The goal of these assessments is to initiate effective risk-informed budget plans for maintenance and repair/replace. Ideally, a degradation model of every component failure mode in the gate would facilitate maintenance decision-making. However, sometimes there is no clear physical understanding how a damage progresses in time; for example, it isn't clear how the bearing gaps change in time in the quoin blocks of a miter gate. Therefore, this is one motivation for the framework proposed in this paper, which integrates Structural Health Monitoring with a Markov transition matrix built from historical condition assessment. To show the applicability of this framework, two examples are presented of how to find the optimal time to plan for maintenance of components in miter gates i) static maintenance planning based on operational condition assessment (OCA) ratings only and ii) dynamic maintenance planning based on integration of damage diagnostics based on monitoring data and failure prognosis based on OCA ratings. In addition, this paper presents a new Bayesian approach to estimate the ratio of errors in the OCA ratings, which allows for improved accuracy in OCA rating-based prognosis.
机译:现场工程师经常使用条件评估和评级系统来评估内陆导航资产和组件。这些评估的目标是启动有效的维护和修理/替换的风险预算计划。理想情况下,门中每个组件故障模式的降级模型将促进维护决策。但是,有时候没有明确的身体理解损坏及时的进展情况;例如,它不清楚轴承差距如何在坐标栅极的Quoin块中变化。因此,这是本文提出的框架的一种动机,其与从历史条件评估的Markov转换矩阵集成了结构健康监测。为了展示本框架的适用性,提出了两个示例如何找到基于操作条件评估(OCA)评级的斜门I)静态维护规划的最佳时间来查找维护组件的最佳时间。(OCA)额定值,II)动态维护规划基于监测数据和基于OCA等级的监测数据和失败预测的损伤诊断整合。此外,本文提出了一种新的贝叶斯方法来估计OCA评级中误差比,这允许改善基于OCA等级的预后的准确性。

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