Feedback Data Processing for Maintenance Optimization and Grouping—An Application to Road Markings

In recent years, the maintenance of multicomponent systems has been discussed in many papers. The aim of these studies is to use the maintenance duration of one component for the maintenance of other components to minimize the total maintenance cost of the system. The complexity of the maintenance o...

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Vydáno v:Future transportation Ročník 3; číslo 2; s. 768 - 790
Hlavní autoři: Najeh, Ikram, Daucher, Dimitri, Redondin, Maxime, Bouillaut, Laurent
Médium: Journal Article
Jazyk:angličtina
Vydáno: Chicago MDPI AG 01.06.2023
MDPI
Témata:
Age
Artificial intelligence
Data processing
Engineering Sciences
grouping maintenance strategy
Infrastructure
Maintenance costs
Optimization
Preventive maintenance
retroreflection
road marking
Roads & highways
transportation infrastructure reliability
Weibull analysis
retroreflection
road marking
transportation infrastructure reliability
grouping maintenance strategy
Weibull analysis
ISSN:2673-7590, 2673-7590
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Abstract In recent years, the maintenance of multicomponent systems has been discussed in many papers. The aim of these studies is to use the maintenance duration of one component for the maintenance of other components to minimize the total maintenance cost of the system. The complexity of the maintenance of this kind of system is due to its structure and its large number of components. The present paper suggests a grouped maintenance policy for multicomponent systems in a finite planning horizon based on the systemic inspection feedback data. The system considered is periodically inspected. Then, the collected data are triply censored (left, right, and interval censored). The proposed grouped maintenance strategy starts by clustering the components into g clusters according to their degradation model. Then, an expectation minimization algorithm is applied to correct the censorship in the data and to associate a Weibull distribution with each cluster. The proposed grouped maintenance strategy begins by specifying an individual maintenance plan for each cluster by identifying an optimal replacement path. Then, this step is followed by finding an optimal grouping strategy using a genetic algorithm. The aim is to identify a point in time when the components can be maintained simultaneously. To illustrate the proposed strategy, the grouped maintenance policy is applied to the feedback data of the road markings of French National Road 4 (NR4) connecting Paris and Strasbourg.
AbstractList In recent years, the maintenance of multicomponent systems has been discussed in many papers. The aim of these studies is to use the maintenance duration of one component for the maintenance of other components to minimize the total maintenance cost of the system. The complexity of the maintenance of this kind of system is due to its structure and its large number of components. The present paper suggests a grouped maintenance policy for multicomponent systems in a finite planning horizon based on the systemic inspection feedback data. The system considered is periodically inspected. Then, the collected data are triply censored (left, right, and interval censored). The proposed grouped maintenance strategy starts by clustering the components into g clusters according to their degradation model. Then, an expectation minimization algorithm is applied to correct the censorship in the data and to associate a Weibull distribution with each cluster. The proposed grouped maintenance strategy begins by specifying an individual maintenance plan for each cluster by identifying an optimal replacement path. Then, this step is followed by finding an optimal grouping strategy using a genetic algorithm. The aim is to identify a point in time when the components can be maintained simultaneously. To illustrate the proposed strategy, the grouped maintenance policy is applied to the feedback data of the road markings of French National Road 4 (NR4) connecting Paris and Strasbourg.
Author Najeh, Ikram
Bouillaut, Laurent
Redondin, Maxime
Daucher, Dimitri
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  surname: Bouillaut
  fullname: Bouillaut, Laurent
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Issue 2
Keywords retroreflection
road marking
transportation infrastructure reliability
grouping maintenance strategy
Weibull analysis
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SubjectTerms Age
Artificial intelligence
Data processing
Engineering Sciences
grouping maintenance strategy
Infrastructure
Maintenance costs
Optimization
Preventive maintenance
retroreflection
road marking
Roads & highways
transportation infrastructure reliability
Weibull analysis
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Title Feedback Data Processing for Maintenance Optimization and Grouping—An Application to Road Markings
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