Availability based maintenance scheduling in Domestic Hot water of HVAC system
Notice bibliographique
Résumé
Reliability centered maintenance is an analytical tool for preventive maintenance planning. The availability based maintenance method is a branch of reliability centered maintenance, which considers mean time to failure (MTTF) and mean time to repair (MTTR). MTTF of a system is identified from the reliability distribution of its components, and MTTR defines maintenance period of components. In this sense, a reliability function is determined from historic failure data of components during their operation period (in form of a bathtub curve). MTTF is calculated based on this reliability function. This thesis is based on availability based maintenance on the domestic hot water (DHW) of HVAC system, which incorporates the time needed for maintenance of components in availability analysis. The Keeping system availability (KSA) method provides maintenance scheduling by considering the outcomes of the maintenance on the DHW system, while maintaining the availability of the current system. This method has been developed in the maintenance scheduling of power plants as the continual availability of the power generation systems is a critical issue. We have adopted this approach for DHW system of HVAC, which is a critical component in provision of hot water during long cold seasons in Canada. The availability based maintenance approach with KSA decision process has been developed to optimize the maintenance schedule of components in order to prevent over-maintenance. For this purpose, we rely on MTTF and MTTR. MTTF is quantified by the reliability function in a component, and its value should be modified based on pre-defined scenarios, which indicate average maintenance interval (AMI) types. Then, the existing components with a different maintenance times are sorted according to the maintenance effect on keeping the availability of the system, while reducing the maintenance cost. The sorting list is divided into two groups: top loop (components with low maintenance effect), and bottom loop (components with high maintenance effect). After running the KSA decision process, the outcomes consist of different “STEP NO” with different combinations of maintenance scenarios in the existing components in the DHW system. The main criterion in selection of the “STEP NO” is to have the modified availability (system with maintenance plan) equal to or greater than the current system availability (system without maintenance plan). In the next step, we examine changing the arrangement of the heat transfer sub-system from parallel to standby in order to reduce maintenance cost, while keeping the availability of the system at the same level. In addition, a replacement analysis is performed on the heat exchanger to identify the replacement time in its repairable subcomponents. Finally, a life cycle cost (LCC) analysis is performed to compare the maintenance cost and replacement cost between these two options.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».