An α-vector predictive value iteration algorithm for transportation infrastructure maintenance under partially observable conditions

IF 11 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL
Chunhui Guo, Zhenglin Liang
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引用次数: 0

Abstract

Transportation infrastructure is degrading over time and poses the risk of failure when exposed to a dynamic environment. Periodic inspection is often implemented to assess the requirement of maintenance. However, the inspected conditions can only partially reflect the underlying degradation, complicating the decision of maintenance. Moreover, inspections of early degradation often have no value-adding to condition improvement and incur a portion of unnecessary expenses. To address the abovementioned issues, we propose a sequential predictive maintenance policy that accounts for the partial observation of the infrastructure’s condition to reduce unnecessary inspections. The schedule of inspection timings is predicted according to the estimated Remaining Useful Life distribution, adaptive to stochastic degradation. We demonstrate that the optimal value function is piecewise linear and convex when decision epochs are non-periodic. Leveraging this insight, we have designed an α-vector Predictive Value Iteration algorithm (α-PVI) to optimize the transportation infrastructure maintenance policy. The α-PVI algorithm further reduces the time complexity compared with the Point-Based Value Iteration algorithm. Our designed approach is verified through an application for the maintenance optimization of pavements and bridges. The results demonstrate that the α-PVI algorithm reduces unnecessary inspection costs by on average 61.25% when compared to the periodic inspection approach. The α-PVI algorithm constructs a new paradigm of predictive maintenance under partially observable conditions.
部分可观测条件下交通基础设施维护的α-向量预测值迭代算法
随着时间的推移,交通基础设施正在退化,并且在暴露于动态环境中时存在故障风险。定期检查通常用于评估维修需求。然而,所检查的条件只能部分反映潜在的退化,使维修决策复杂化。此外,对早期退化的检查通常对条件改善没有任何价值,而且会招致一部分不必要的费用。为了解决上述问题,我们提出了一种顺序预测性维护策略,该策略考虑了对基础设施状况的部分观察,以减少不必要的检查。根据估计的剩余使用寿命分布预测检查时间计划,适应随机退化。证明了决策周期为非周期时的最优值函数是分段线性的和凸的。利用这一见解,我们设计了一个α-向量预测值迭代算法(α-PVI)来优化交通基础设施维护策略。与基于点的值迭代算法相比,α-PVI算法进一步降低了时间复杂度。通过对路面和桥梁养护优化的应用验证了我们的设计方法。结果表明,与定期检测方法相比,α-PVI算法平均减少了61.25%的不必要检测成本。α-PVI算法构建了部分可观测条件下的预测性维修新范式。
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来源期刊
Reliability Engineering & System Safety
Reliability Engineering & System Safety 管理科学-工程:工业
CiteScore
15.20
自引率
39.50%
发文量
621
审稿时长
67 days
期刊介绍: Elsevier publishes Reliability Engineering & System Safety in association with the European Safety and Reliability Association and the Safety Engineering and Risk Analysis Division. The international journal is devoted to developing and applying methods to enhance the safety and reliability of complex technological systems, like nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, constructed infrastructure, and manufacturing plants. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to balance academic material and practical applications.
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