Data‐driven maintenance planning and scheduling based on predicted railway track condition

Sedghi, Mahdieh; Bergquist, Bjarne; Vanhatalo, Erik; Migdalas, Athanasios

doi:10.1002/qre.3166

Cited by 14 publications

(3 citation statements)

References 72 publications

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“…The uncertainty of the distribution of the time between failures (TBF) [34] is addressed by [35], who uses a Bayesian approach to estimate the strength of the population of replacement parts, and by [36], who considers the asset's age to update the failure rate of parts. Some PM approaches also consider the health state of components by performing periodic inspections [37] or by using prediction models, such as the Wiener process model [38]. Other studies, such as [39][40][41][42], focused on optimizing PM actions of repairable systems by considering imperfect maintenance actions.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Framework for Maintenance Scheduling Based on Dynamic Preventive Intervals and Remaining Useful Life Estimation

Nunes,

Rocha,

Santos

2024

Future Internet

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Data-based prognostic methods exploit sensor data to forecast the remaining useful life (RUL) of industrial settings to optimize the scheduling of maintenance actions. However, implementing sensors may not be cost-effective or practical for all components. Traditional preventive approaches are not based on sensor data; however, they schedule maintenance at equally spaced intervals, which is not a cost-effective approach since the distribution of the time between failures changes with the degradation state of other parts or changes in working conditions. This study introduces a novel framework comprising two maintenance scheduling strategies. In the absence of sensor data, we propose a novel dynamic preventive policy that adjusts intervention intervals based on the most recent failure data. When sensor data are available, a method for RUL prediction, designated k-LSTM-GFT, is enhanced to dynamically account for RUL prediction uncertainty. The results demonstrate that dynamic preventive maintenance can yield cost reductions of up to 51.8% compared to conventional approaches. The predictive approach optimizes the exploitation of RUL, achieving costs that are only 3–5% higher than the minimum cost achievable while ensuring the safety of critical systems since all of the failures are avoided.

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Section: Related Workmentioning

confidence: 99%

Adaptive Framework for Maintenance Scheduling Based on Dynamic Preventive Intervals and Remaining Useful Life Estimation

Nunes,

Rocha,

Santos

2024

Future Internet

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“…On the other hand, CBM is typically applied to systems whose degradation states are available. CBM has been increasingly adopted in various fields, including railway, 14 subsea tree systems, 15 and wind turbines 16 . Studies have shown that CBM can effectively improve system reliability by utilizing the condition information for maintenance decision making 17–19 …”

Section: Introductionmentioning

confidence: 99%

Non‐periodic inspection and replacement policy of system subject to non‐homogeneous gamma degradation process

Yao,

Hu,

et al. 2023

Quality & Reliability Eng

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The non‐homogeneous gamma process can exhibit a convex average trend with a non‐linear shape parameter, providing significant flexibility in fitting an accelerated degradation process. However, few studies have been conducted on the maintenance modeling of systems undergoing a non‐homogeneous gamma degradation process despite its versatility. Meanwhile, most maintenance studies adopt a periodic inspection policy to reveal the degradation level. However, it is reasonable to inspect less frequently when the degradation level of the system is low and to perform inspections more frequently when the degradation level increases. Therefore, we conduct a study on the inspection/replacement policy of systems undergoing a non‐homogeneous gamma degradation process, where three non‐periodic inspection policies are investigated. We formulate the problem as a SMDP framework and present a value iteration algorithm to obtain the optimal action at each decision epoch that minimizes the long‐term average cost. A comprehensive numerical study is provided to illustrate the effectiveness of our proposed model.

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“…5,6 Planned maintenance and unplanned maintenance are two ways of maintenance. [7][8][9] The former is known as preventive maintenance 10 while the latter is called corrective maintenance. 11,12 For a set of equipment, some of the subsystems require scheduled maintenance according to the maintenance manual from manufacturers.…”

Section: Introductionmentioning

confidence: 99%

Optimal maintenance period and maintenance sequence planning under imperfect maintenance

Zhang

Jiang

Zheng

et al. 2022

Quality & Reliability Eng

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Maintenance is essential for today's equipment or systems and maintenance costs account for a significant portion of the overall expenditure of devices. Maintenance period optimization and maintenance sequence planning are primary aspects of maintainability. To this end, this paper proposes an approach for maintenance period optimization and maintenance sequence planning, with the consideration of imperfect maintenance. A model of the total maintenance cost per unit time is set up, and the optimal time period for preventive maintenance is calculated. In addition, a new index is created to evaluate the maintenance efficiency of optimal maintenance sequences in different scenarios. The performance of the proposed method is validated by a maintenance sequence optimization and maintenance sequence planning of baggage handling systems. The results indicate that the proposed method is efficient and contributes to maintenance cost savings.

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Data‐driven maintenance planning and scheduling based on predicted railway track condition

Cited by 14 publications

References 72 publications

Adaptive Framework for Maintenance Scheduling Based on Dynamic Preventive Intervals and Remaining Useful Life Estimation

Adaptive Framework for Maintenance Scheduling Based on Dynamic Preventive Intervals and Remaining Useful Life Estimation

Non‐periodic inspection and replacement policy of system subject to non‐homogeneous gamma degradation process

Optimal maintenance period and maintenance sequence planning under imperfect maintenance

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