Systems subject to repair and maintenance actions: Modeling and optimization
Notice bibliographique
Résumé
In maintenance and reliability, the use of systems that are repairable is growing every year, as consumers and manufacturers are gradually moving away from "throw-away" products for economical and environmental reasons. In contrast to non-repairable systems for which the only decision to be made is the "when to replace" decision, for repairable systems the decisions to be made are more complex. Not only needs the "when to replace" decision be addressed, but in addition also the "when to repair" and "what to repair" decisions. This makes the optimization of repairable systems complex. Furthermore, the formulation of repairable systems optimization problems is influenced by the business context that surrounds the system under consideration. Therefore, at least in theory, numerous repairable system optimization models can be formulated, where each is defined by the system itself and the business context it belongs to. The first model, called the General Repair Restriction Model (GRRM), addresses the question when to replace or repair a repairable system over a finite horizon when the number of repairs to which the system can be subjected to is restricted. Also, the system owner does not have information on the detailed repair activities that are being carried out, and/or cannot control the "what to repair" decision due to the outsourcing of system repairs to specialized repair centres. A dynamic programming approach will be used to derive the structure of the optimal policies. The second model, called the Repair and Maintenance Indicator Model (RMI), addresses the same question when the system owner does have access to detailed repair information and can control the "what to repair" decision. The RMI model is a new repairable system model, and is aimed to accommodate a greater variety of repairable systems. Besides the capability to address the "what to repair" question, the RMI model can also identify the most critical parts of a system, which may be of particular importance to OEMs (Original Equipment Manufacturers) when prioritizing design modifications aimed to improve system reliability. The general purpose of the RMI model (in contrast to models previously published in the literature) is to establish a clear decision rule in terms of the parts to be replaced in each repair, and therefore goes beyond the traditional "age-reduction" or "intensity-reduction" factors which have been frequently used to specify the "degree of repair" whenever the system is in the repair shop. This will be of particular benefit for tradepersons in the repair shop, as the RMI model will establish not only the "degree of repair" but also how to accomplish this "degree" in more practical terms. Besides the theoretical results, for both models several examples and case studies will be presented. The case studies are based on real problems commonly encountered in industry. The case studies show that both models are realistic, with considerable practical applications. In this thesis, two repairable system optimization models will be addressed. These two models are based on two commonly found business contexts in industry (in particular the mining industry), but have not been addressed so far in the maintenance and reliability literature.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».