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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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Future Transportation |
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| Online Access: | https://www.mdpi.com/2673-7590/3/2/44 |
| _version_ | 1797594753843331072 |
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| author | Ikram Najeh Dimitri Daucher Maxime Redondin Laurent Bouillaut |
| author_facet | Ikram Najeh Dimitri Daucher Maxime Redondin Laurent Bouillaut |
| author_sort | Ikram Najeh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T02:26:41Z |
| format | Article |
| id | doaj.art-b94051424a4447348ac54ad21d6ad92a |
| institution | Directory Open Access Journal |
| issn | 2673-7590 |
| language | English |
| last_indexed | 2024-03-11T02:26:41Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Transportation |
| spelling | doaj.art-b94051424a4447348ac54ad21d6ad92a2023-11-18T10:31:03ZengMDPI AGFuture Transportation2673-75902023-06-013276879010.3390/futuretransp3020044Feedback Data Processing for Maintenance Optimization and Grouping—An Application to Road MarkingsIkram Najeh0Dimitri Daucher1Maxime Redondin2Laurent Bouillaut3VEDECOM Institute, 78000 Versailles, FranceUniversité Gustave Eiffel, PICS-L, 77420 Champs-sur-Marne, FranceColas SA, CORE Center, 75730 Paris, FranceUniversité Gustave Eiffel, GRETTIA, 77420 Champs-sur-Marne, FranceIn 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.https://www.mdpi.com/2673-7590/3/2/44grouping maintenance strategyroad markingtransportation infrastructure reliabilityretroreflectionWeibull analysis |
| spellingShingle | Ikram Najeh Dimitri Daucher Maxime Redondin Laurent Bouillaut Feedback Data Processing for Maintenance Optimization and Grouping—An Application to Road Markings Future Transportation grouping maintenance strategy road marking transportation infrastructure reliability retroreflection Weibull analysis |
| title | Feedback Data Processing for Maintenance Optimization and Grouping—An Application to Road Markings |
| title_full | Feedback Data Processing for Maintenance Optimization and Grouping—An Application to Road Markings |
| title_fullStr | Feedback Data Processing for Maintenance Optimization and Grouping—An Application to Road Markings |
| title_full_unstemmed | Feedback Data Processing for Maintenance Optimization and Grouping—An Application to Road Markings |
| title_short | Feedback Data Processing for Maintenance Optimization and Grouping—An Application to Road Markings |
| title_sort | feedback data processing for maintenance optimization and grouping an application to road markings |
| topic | grouping maintenance strategy road marking transportation infrastructure reliability retroreflection Weibull analysis |
| url | https://www.mdpi.com/2673-7590/3/2/44 |
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