Maintenance and Operation of Infrastructure Systems: Review

Sánchez-Silva, Mauricio; Frangopol, Dan M.; Padgett, Jamie E.; Soliman, Mohamed

doi:10.1061/(asce)st.1943-541x.0001543

Cited by 94 publications

(35 citation statements)

References 186 publications

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“…According to this rising practical requirement, the PdM has also received significant attention in literature over the last decade, see [1][2][3] for recent overviews. Generally, the predicted maintenance framework consists of two connected key parts: prediction of the system residual useful life time (RUL) and making decisions.…”

Section: Introductionmentioning

confidence: 99%

A new dynamic predictive maintenance framework using deep learning for failure prognostics

Nguyen

Medjaher

2019

Reliability Engineering & System Safety

233

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Section: Introductionmentioning

confidence: 99%

A new dynamic predictive maintenance framework using deep learning for failure prognostics

Nguyen

Medjaher

2019

Reliability Engineering & System Safety

233

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“…Maintenance has been recognized as one of the most important measures for ensuring a system's reliability and widely studied . However, different from traditional maintenance planning problems with maintenance being conducted during the breaks or downtime of a system, the above mentioned networked systems need to keep the prescribed demand continuously satisfied, even during the maintenance actions being conducted (eg, people may need to turn on lights at any time, although the lights need not to be kept on all the time).…”

Section: Introductionmentioning

confidence: 99%

A bi‐objective optimization approach for the maintenance planning of networked systems

Zhang

2020

Quality & Reliability Eng

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Networked systems, such as telecommunications, transportation, and power transmission, are critical for the economic development and social well-being of a society and are required to keep the prescribed demand continuously satisfied. Therefore, when planning maintenance actions for such a system, the adverse influence on its reliability caused by the unavailability of the components in the process of being maintained needs to be taken into account. To deal with this problem, we propose a new bi-objective optimization approach to determine the Pareto optimal maintenance plans for a networked system, simultaneously maximizing its reliability within the concerned planning horizon and minimizing the total maintenance cost. Both perfect and imperfect maintenance actions are considered. The proposed approach can well balance the influence of maintenance actions on a networked system's reliability during their implementation and after being completed and, thus, can help ensure its reliability of continuously satisfying the prescribed demand. K E Y W O R D Sbi-objective optimization, continuous demand, maintenance optimization, networked system, unavailability

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“…Bridges are important infrastructures which are commonly used in transportation networks, and it is vital for them to function up to the acceptable level during their service life However, to keep their functionality acceptable maintaining them and corresponding cost is unavoidable. In United States, the annual cost of rehabilitation is $7 billion [3]. To optimize the functionality of bridge infrastructure, continuous bridge access must be balanced with repair and maintenance costs as well as safety consideration [1].…”

Section: Introductionmentioning

confidence: 99%

Predicting Bridge Conditions in Ontario: A Case Study

Taghaddos¹,

Mohamed²

2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Maintenance and repair of bridges represent significant costs in provincial and municipal government budgets. Prediction of bridge conditions can help managers in annual cost estimating and budget allocation. To assess Bridge Condition Index (BCI), each bridge component must be inspected every two years, tested if it is required, and rated. Bridge condition can be affected over time by different attributes such as material, structure, location, and use. This paper presents a study conducted to model and predict BCI based on a historical dataset of 2803 bridges in Ontario from 2000 to 2014. The paper describes the work related to data collection, cleaning and transformation. In addition, a comparison of the cross-validation performance of alternative BCI prediction models is presented and discussed.

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Maintenance and Operation of Infrastructure Systems: Review

Cited by 94 publications

References 186 publications

A new dynamic predictive maintenance framework using deep learning for failure prognostics

A new dynamic predictive maintenance framework using deep learning for failure prognostics

A bi‐objective optimization approach for the maintenance planning of networked systems

Predicting Bridge Conditions in Ontario: A Case Study

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