Effect of Inspection Errors in Optimal Maintenance Decisions for Deteriorating Quoin Blocks in Miter Gates

2022 ◽  
pp. 1-4
Author(s):  
Manuel A. Vega ◽  
Zhen Hu ◽  
Michael D. Todd
Keyword(s):  
Inspection Errors ◽  
Optimal Maintenance ◽  
Maintenance Decisions

Survival analysis at multiple scales for the modeling of track geometry deterioration

2017 ◽  
Vol 232 (3) ◽  
pp. 842-850 ◽  
Author(s):  
Negin Alemazkoor ◽  
Conrad J Ruppert ◽  
Hadi Meidani
Keyword(s):  
Survival Analysis ◽  
Multiple Scales ◽  
Fine Scale ◽  
Coarse Scale ◽  
Track Geometry ◽  
Time Period ◽  
Scale Models ◽  
Optimal Maintenance ◽  
Maintenance Decisions

Defects in track geometry have a notable impact on the safety of rail transportation. In order to make the optimal maintenance decisions to ensure the safety and efficiency of railroads, it is necessary to analyze the track geometry defects and develop reliable defect deterioration models. In general, standard deterioration models are typically developed for a segment of track. As a result, these coarse-scale deterioration models may fail to predict whether the isolated defects in a segment will exceed the safety limits after a given time period or not. In this paper, survival analysis is used to model the probability of exceeding the safety limits of the isolated defects. These fine-scale models are then used to calculate the probability of whether each segment of the track will require maintenance after a given time period. The model validation results show that the prediction quality of the coarse-scale segment-based models can be improved by exploiting information from the fine-scale defect-based deterioration models.

Online Maintenance Prioritization via Monte Carlo Tree Search and Case-based Reasoning

2022 ◽  
pp. 1-33
Author(s):  
Michael Hoffman ◽  
Eunhye Song ◽  
Michael Brundage ◽  
Soundar Kumara
Keyword(s):  
Monte Carlo ◽  
Computational Effort ◽  
System Throughput ◽  
Case Based Reasoning ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Markov Decision ◽  
Optimal Maintenance ◽  
Maintenance Decisions ◽  
Case Based

Abstract When maintenance resources in a manufacturing system are limited, a challenge arises in determining how to allocate these resources among multiple competing maintenance jobs. We formulate this problem as an online prioritization problem using a Markov decision process (MDP) to model the system behavior and Monte Carlo tree search (MCTS) to seek optimal maintenance actions in various states of the system. Further, we use Case-based Reasoning (CBR) to retain and reuse search experience gathered from MCTS to reduce the computational effort needed over time and to improve decision-making efficiency. We demonstrate that our proposed method results in increased system throughput when compared to existing methods of maintenance prioritization while also reducing the time needed to identify optimal maintenance actions as more experience is gathered. This is especially beneficial in manufacturing settings where maintenance decisions must be made quickly.

Optimal Maintenance Decisions for Metro Tunnels in Soft Soil

2016 ◽  
pp. 1683-1690
Author(s):  
Q. Ai ◽  
Y. Yuan ◽  
X. Jiang ◽  
S. Mahadevan
Keyword(s):  
Soft Soil ◽  
Optimal Maintenance ◽  
Maintenance Decisions

scholarly journals OPTIMAL MAINTENANCE DECISIONS FOR DIKES

2000 ◽  
Vol 14 (1) ◽  
pp. 101-121 ◽  
Author(s):  
Lennaert J. P. Speijker ◽  
Jan M. van Noortwijk ◽  
Matthijs Kok ◽  
Roger M. Cooke
Keyword(s):  
Decision Model ◽  
Discounted Cost ◽  
Engineering Knowledge ◽  
Initial Cost ◽  
Optimal Maintenance ◽  
Maintenance Decisions ◽  
Mixtures Of Exponentials ◽  
Maintenance Decision ◽  
Maintenance Model

To protect the Dutch polders against flooding, more than 2500 km of dikes have been constructed. Due to settlement, subsoil consolidation, and relative sea-level rise, these dikes slowly sink “away into the sea” and should therefore be heightened regularly (at present, every 50 years). In this respect, one is interested in safe and cost-optimal dike heightenings for which the sum of the initial cost of investment and the future (discounted) cost of maintenance is minimal.For optimization purposes, a maintenance model has been developed for dikes subject to uncertain crest-level decline. On the basis of engineering knowledge, crest-level decline has been modeled as a monotone stochastic process with expected decline being either linear or nonlinear (i.e., linear after transformation) in time. For both models and for a particular unit time, the increments are distributed according to mixtures of exponentials.In a case study, the maintenance decision model has been applied to the problem of heightening the Dutch “Oostmolendijk.”

scholarly journals Determination of Optimal MR&R Strategy and Inspection Intervals to Support Infrastructure Maintenance Decision Making

2021 ◽  
Vol 13 (5) ◽  
pp. 2664
Author(s):  
Yingnan Yang ◽  
Hongming Xie
Keyword(s):  
Decision Making ◽  
Transition Probabilities ◽  
Model Development ◽  
Cost Savings ◽  
Time Period ◽  
Predetermined Level ◽  
Optimal Maintenance ◽  
Maintenance Decisions ◽  
Infrastructure Maintenance

In the commonly used approach to maintenance scheduling for infrastructure facilities, maintenance decisions are made under the assumptions that inspection frequency is periodical and fixed, and that the true state of a facility is revealed through inspections. This research addresses these limitations by proposing a decision-making approach for determining optimal maintenance, repair, and rehabilitation (MR&R) strategy and inspection intervals for infrastructure facilities that can explicitly take into account non-periodical inspections as well as previously considered periodical inspections. Four transition probabilities are proposed to represent four different MR&R strategies. Then, an optimization program is suggested to minimize MR&R and inspection costs of a bridge element network over a given time period, while keeping the condition states of the element network above a predetermined level. A case study was applied to illustrate the proposed approach. The results show that the proposal approach can support decision making in situations where non-periodical inspections and MR&R actions are incorporated into the model development. If employed properly, this may allow agencies to maintain their infrastructure more effectively, resulting in cost savings and reducing unnecessary waste of resources.

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