Готові списки джерел за темами / Prescriptive maintenance

Добірка наукової літератури з теми "Prescriptive maintenance"

Автор: Grafiati
Опубліковано: 7 липня 2024

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  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Статті в журналах з теми "Prescriptive maintenance":

1

Geier, Manuel. "Prescriptive Maintenance mit zentralem Datenportal." BWK ENERGIE. 74, no. 9-10 (2022): 26–27. http://dx.doi.org/10.37544/1618-193x-2022-9-10-26.

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Wollen Unternehmen den optimalen Instandhaltungsmix finden, benötigen sie zunächst saubere Maschinendaten. Diese müssen richtig erfasst, konsolidiert und danach bewertet werden, um aus der Analyse Einsichten zu gewinnen, mit denen unerwartete Stillstände vermieden werden und die Instandhaltung gleichzeitig konstant optimiert werden kann. Unternehmen der Energiebranche gelingt das mit einem zentralen Datenportal.
2

Errandonea, Itxaro, Unai Alvarado, Sergio Beltrán, and Saioa Arrizabalaga. "A Maturity Model Proposal for Industrial Maintenance and Its Application to the Railway Sector." Applied Sciences 12, no. 16 (August 17, 2022): 8229. http://dx.doi.org/10.3390/app12168229.

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Maintenance is one of the major concerns of the industrial sector. Acquiring better levels of maintenance maturity is one of the objectives to be achieved. Therefore, prescriptive maintenance is one of the areas of recent research. Current works in literature are focused on specifics of maintenance strategies (from preventive to prescriptive), usually related to a fixed asset. No previous work has been identified regarding the methodology and guidelines to be followed to be able to evolve within the different strategies from a generic perspective. To address the lack of a methodology that shows a more evolutionary path between maintenance strategies, this paper presents Maintenance Maturity Model M3: a maturity model that identifies three areas of action, four levels of maturity, and the activities to be carried out in each of them to make progress in the maturity level of maintenance strategies. The implementation of prescriptive maintenance should be done in a gradual way, starting at the lowest levels. M3 approaches the problem from a broader perspective, analyzing the 18 different domains and the different levels of prior maturity to be considered for prescriptive maintenance. A study has also been carried out on the different maintenance actions and the applicability of the proposed M3 maturity model to the railway infrastructure maintenance is discussed. In addition, this paper also highlights future research lines and open issues.
3

Yamashita, Akiko, Mitsuyuki Kawakami, Yoshimasa Inagaki, and Takao Ohkubo. "A Prescriptive Exercise Program for Health Maintenance." International Journal of Occupational Safety and Ergonomics 7, no. 2 (January 2001): 195–209. http://dx.doi.org/10.1080/10803548.2001.11076486.

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4

Kothona, Despoina, Ioannis P. Panapakidis, and Georgios C. Christoforidis. "Development of prescriptive maintenance methodology for maintenance cost minimization of photovoltaic systems." Solar Energy 271 (March 2024): 112402. http://dx.doi.org/10.1016/j.solener.2024.112402.

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5

Kawakami, M., A. Yamashita, T. Ohkubo, and Y. Sanbayashi. "A Reasonable Prescriptive Exercise Program for Health Care Maintenance." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 33 (July 2000): 6–178. http://dx.doi.org/10.1177/154193120004403327.

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6

Koops, Lily Geraldine. "Optimized Maintenance Decision-Making – A Simulation-supported Prescriptive Analytics Approach based on Probabilistic Cost-Benefit Analysis." PHM Society European Conference 5, no. 1 (July 22, 2020): 14. http://dx.doi.org/10.36001/phme.2020.v5i1.1269.

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Prescriptive Maintenance strategies are emerging as potential next level of reliability and maintenance best practice. Likely outcomes of maintenance alternatives and their effects on e.g. cost and safety are comparatively evaluated by exploiting various sources of data, knowledge and models. By this means, optimized courses of actions are recommended to quickly resolve problems and to automate Maintenance, Repair and Overhaul (MRO) decisions. In this work, the key question is pursued as to how their dependability and potential business advantage can be assessed and improved in the presence of uncertainty and variability of various decision-influencing factors such as degradation and maintenance model parameters and cost sources. For this purpose, a step-by-step procedure to optimal solution prescription and potential / risk assessment is developed based on a probabilistic approach to cost-benefit analysis and on the definition of relevant metrics. By the help of a Wiener process degradation model capable of implementing random effects of imperfect repairs and a Monte Carlo simulation, its value is illustrated by a use case example – repair / replacement decision support in the aeronautical context. The probabilistic approach not only allows to determine, which decision option promises the higher profit and is thus preferred, but also with which risk and potential cost disadvantage it is associated. Furthermore, it uncovers, where higher-quality data or information, can gainfully reduce result uncertainty and hence be assigned a monetary value. It is argued that the presented approach could give industry practitioners directions for identifying and optimizing business cases for Prescriptive Maintenance, by pointing at which sources of data or information are particularly valuable and hence justify dedicated investments for acquiring it. The relevance of the results is discussed specifically with reference to emerging digitized and automated repair processes as well as more generally in the context of future data-trading schemes.
7

Fox, Harriet, Ajit C. Pillai, Daniel Friedrich, Maurizio Collu, Tariq Dawood, and Lars Johanning. "A Review of Predictive and Prescriptive Offshore Wind Farm Operation and Maintenance." Energies 15, no. 2 (January 11, 2022): 504. http://dx.doi.org/10.3390/en15020504.

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Offshore wind farms are a rapidly developing source of clean, low-carbon energy and as they continue to grow in scale and capacity, so does the requirement for their efficient and optimised operation and maintenance. Historically, approaches to maintenance have been purely reactive. However, there is a movement in offshore wind, and wider industry in general, towards more proactive, condition-based maintenance approaches which rely on operational data-driven decision making. This paper reviews the current efforts in proactive maintenance strategies, both predictive and prescriptive, of which the latter is an evolution of the former. Both use operational data to determine whether a turbine component will fail in order to provide sufficient warning to carry out necessary maintenance. Prescriptive strategies also provide optimised maintenance actions, incorporating predictions into a wider maintenance plan to address predicted failure modes. Beginning with a summary of common techniques used across both strategies, this review moves on to discuss their respective applications in offshore wind operation and maintenance. This review concludes with suggested areas for future work, underlining the need for models which can be simply incorporated by site operators and integrate live data whilst handling uncertainties. A need for further focus on medium-term planning strategies is also highlighted along with consideration of the question of how to quantify the impact of a proactive maintenance strategy.
8

Belov, Sergei, Sergei Nikolaev, and Ighor Uzhinsky. "Hybrid Data-Driven and Physics-Based Modeling for Gas Turbine Prescriptive Analytics." International Journal of Turbomachinery, Propulsion and Power 5, no. 4 (November 9, 2020): 29. http://dx.doi.org/10.3390/ijtpp5040029.

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This paper presents a methodology for predictive and prescriptive analytics of a gas turbine. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnosis of its flame tube. The developed approach allows not just to analyze and predict some problems in the combustion chamber, but also to identify a particular flame tube to be repaired or replaced and plan maintenance actions in advance.
9

Ansari, Fazel, Robert Glawar, and Tanja Nemeth. "PriMa: a prescriptive maintenance model for cyber-physical production systems." International Journal of Computer Integrated Manufacturing 32, no. 4-5 (February 20, 2019): 482–503. http://dx.doi.org/10.1080/0951192x.2019.1571236.

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10

Liu, Bin, Jing Lin, Liangwei Zhang, and Uday Kumar. "A Dynamic Prescriptive Maintenance Model Considering System Aging and Degradation." IEEE Access 7 (2019): 94931–43. http://dx.doi.org/10.1109/access.2019.2928587.

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Дисертації з теми "Prescriptive maintenance":

1

Dias, Longhitano Pedro. "Maintenance prescriptive pour la gestion optimale de systèmes soumis à dégradation : application à la prescription conjointe de modes d’utilisation et d’actions de maintenance pour des véhicules industriels." Electronic Thesis or Diss., Université Grenoble Alpes, 2024. http://www.theses.fr/2024GRALT010.

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Avec la numérisation, l'internet des objets et la popularisation des services centrés sur les données, différents secteurs économiques ont connu des changements importants dans leur modèle d'entreprise. Pour le secteur automobile en particulier, ces changements concernent l'évolution vers des offres davantage axées sur les services. La vente d'un véhicule n'est plus le dernier contact avec le client, car les services après-vente représentent une part importante du chiffre d'affaires des constructeurs automobiles. Dans cette optique, le principal objectif de ce projet de recherche est de jeter les bases des futurs services pour les véhicules lourds.Ce travail souligne l'importance de l'optimisation de la maintenance ainsi que sa relation avec l'utilisation du véhicule. Dans le passé, la maintenance se limitait à des remplacements correctifs de pièces défectueuses, ce qui entraînait de longues périodes d'inactivité. Cela avait un impact sur l'activité du transporteur, ainsi que sur ses coûts de réparation. L'introduction de systèmes de surveillance et les techniques de communication actuelles permettent de développer de nouvelles méthodes d'optimisation dans lesquelles, non seulement les dates de remplacement sont déterminées de manière optimale, mais aussi l'utilisation du véhicule est modifiée en conséquence, ce qui permet d'optimiser les coûts et d'allonger continuellement la durée de vie utile des camions. Grâce à la technologie actuelle, ces méthodes d'optimisation pourraient être transformées en services qui aident les clients à définir les dates de remplacement, à gérer la logistique pour minimiser les niveaux de dégradation de la flotte, ou même à modifier les paramètres du logiciel du véhicule pour minimiser les coûts à long terme.Cette approche pour la maintenance, dans laquelle la durée de vie est prolongée par une myriade d'actions différentes englobant plusieurs aspects de l'utilisation du véhicule, est souvent appelée maintenance prescriptive. Bien que la maintenance prescriptive ait été dès le départ un élément important de ce travail, sa définition est assez controversée dans la littérature. Par conséquent, un effort conceptuel est fait dans ce document afin de clarifier la portée de ce type de paradigme de maintenance. Avec des définitions plus précises et un champ d'application clair, la maintenance prescriptive est appliquée dans le contexte des véhicules lourds.Les applications de maintenance prescriptive pour les véhicules lourds peuvent être considérées comme des problèmes d'optimisation originaux dans le domaine de la science des transports. Tout au long de l'étude de ces méthodes, des contributions scientifiques significatives ont été apportées. Tout d'abord, la maintenance prescriptive nécessite des modèles qui peuvent relier de manière réaliste la dégradation et l'utilisation du véhicule. De tels modèles sont difficilement adaptés aux problèmes de prise de décision et nécessitent une adaptation. Dans ce document, ces modèles sont étudiés en détail.La résolution des problèmes d'optimisation susmentionnés est une tâche non triviale. Toutes les formulations susmentionnées nécessitent un effort de calcul important pour être résolues avec exactitude. Ainsi, une autre dimension de la contribution de ce travail est le développement d'algorithmes adaptés à la résolution de ces problèmes. Les méthodes classiques sont adaptées en tirant parti des propriétés particulières des modèles utilisés et des heuristiques capables de combler l'écart d'optimisation en un temps raisonnable sont développées
With digitalization, the internet of things and the popularization of data-centric services, different economy sectors have gone through significant changes in their business model. For the automotive sector in particular, those changes relate to moving towards more service-oriented offers. Selling a vehicle is no longer the last contact with the client, as aftermarket services are responsible for a significant part of vehicle manufacturer's revenue. With that in mind, the main concern of this research project is to lay down the foundations for enabling future services for heavy vehicles.This work stresses the importance of maintenance optimization as well as its relationship with vehicle usage. In the past, maintenance was limited to corrective replacements of faulty parts resulting in long idle periods. This impacted the activity of the transporter, as well as its repair costs. The introduction of monitoring systems along with the current communication techniques allow the development of new optimization methods in which, not only replacement dates are determined optimally but also, vehicle usage is changed accordingly, ensuring cost optimization, and continuously extending trucks remaining useful life. With today's technology, those optimization methods could be turned into services that help clients defining replacement dates, manage logistics to minimize degradation levels of the fleet, or even change vehicle software parameters to minimize the long-term costs.This approach to maintenance, in which life is extend through a myriad of different actions encompassing several aspects of vehicle usage, is often referred to as prescriptive maintenance. Although prescriptive maintenance was from the beginning an important element of this work, its definition is rather controversial in the literature. As a consequence, a conceptual effort is done in this document in order to clarify the scope of this kind of maintenance paradigm. With more precise definitions and a clear scope, prescriptive maintenance is applied in the context of heavy vehicles.Prescriptive maintenance applications for heavy vehicles can be seen as original optimization problems in the realm of transportation science. Throughout the investigation of such methods, significant scientific contributions were made. First and foremost, prescriptive maintenance requires models that can realistically connect degradation and vehicle usage. Such models are hardly adapted for decision-making problems and require adaptation. In this document, such models are studied in detail.Solving those aforementioned optimization problems is a non-trivial task. All of the aforementioned formulations required significant computational effort to be solved exactly. As such, another dimension of this work contribution is the development of algorithms suitable for solving them. Classical methods are adapted taking advantage of particular properties of the models used and heuristics capable of closing the optimization gap in a reasonable time are developed
2

Ngorsuraches, Surachat. "The effects of inflation, utilization, prescription size, and product mix on prescription drug expenditure changes in a health maintenance organization (HMO)." Madison, 2002. http://www.library.wisc.edu/databases/connect/dissertations.html.

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3

Richards, Erika Kristin McKeithan. "An analysis of the effect of managed care implementation of prescription drug utilization by Texas Medicaid clients." Access restricted to users with UT Austin EID Full text (PDF) from UMI/Dissertation Abstracts International, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3035965.

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4

Balasubramanian, Prashanth. "Root cause analysis-based approach for improving preventive/corrective maintenance of an automated prescription-filling system." Diss., Online access via UMI:, 2009.

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Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Engineering, 2009.
Includes bibliographical references.
5

Johnsrud, Michael Thomas. "An analysis of the utilization of and payments for prescription drugs and related health care services for Medicaid clients in health maintenance organization (HMO) and primary care case management (PCCM) health care delivery systems in Texas /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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6

Kubíček, Lukáš. "Příprava a realizace výstavby mateřské školy." Master's thesis, Vysoké učení technické v Brně. Fakulta stavební, 2019. http://www.nusl.cz/ntk/nusl-392313.

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The aim of my thesis is the Construction technological project of nursery school building. The work involves technical report building equipment, technological instruction for selected activities, inspection and test plans, design of mechanical assemblies, transport links, schedule of health and safety, as well as environmental protection, itemized project budget, timetable and building maintenance instructions
7

Faltynková, Pavla. "Bytové domy A a B v Nemocniční ulici v Uničově - stavebně technologická příprava stavby." Master's thesis, Vysoké učení technické v Brně. Fakulta stavební, 2012. http://www.nusl.cz/ntk/nusl-225557.

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This thesis deals with constructive -technological solutions of construction of two residential buildings A and B in Uničov. The thesis includes preparation and construction site equipment, technological prescription for implementation of anhydrite floors, time schedule and financial plan of construction, project budget, monitoring and test plans, instructions for maintenance, fire problem solution and security plan for requirements of the EMS.
8

Mouka, Jiří. "Víceúčelový objekt v Lužici - stavebně technologický projekt." Master's thesis, Vysoké učení technické v Brně. Fakulta stavební, 2018. http://www.nusl.cz/ntk/nusl-371979.

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The diploma thesis deals with the solution of the multi-purpose building project in Lužice. This work includes the extended accompanying and summary technical report, the coordination situation of the construction, the technical report of the transport relations and the coordination of the oversized transport, the object time schedule and the financial plan of the construction, the plan for providing the material resources containing the staff balance for the construction of the main building, the time schedule for the assembly of the prefabricated skeleton, technological standard for prefabricated skeleton, design of main building machines and mechanisms including their time commitment, organization of construction of multipurpose building including drawings of construction site equipment, technological regulation for assembling of prefabricated skeleton and for making of reinforced concrete floor, inspection and testing plan for prefabricated skeleton assembly, lifting mechanism, assembly schemes for prefabricated skeleton elements, lifting mechanism verification schemes, item budget prefabricated skeleton chalk and instructions for building use and maintenance.
9

Guichard, Anne. "Prescription et usages du Subutex dans les parcours des toxicomanes en France." Thèse, 2005. http://hdl.handle.net/1866/17756.

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10

Ngorsuraches, Surachat. "The effects of price change and product mix on prescription drug expenditures in a health maintenance organization (HMO)." 1999. http://catalog.hathitrust.org/api/volumes/oclc/44408529.html.

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Thesis (M.S.)--University of Wisconsin--Madison, 1999.
Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 107-111).

Книги з теми "Prescriptive maintenance":

1

McCann, Michael L. Prescription for Health: Healthy RX for Prevention, Maintenance and Recovery. Lanham, MD, USA: Pneuma Life Publishing, 2002.

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2

Green, Mark. Compromising your drug of choice: How HMOs are dictating your next prescription. New York, NY: [New York Public Advocate's Office], 1996.

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3

Scanlon, William. Prescription drug benefits: Impact of Medicare HMOs' use of formularies on beneficiaries : statement of William J. Scanlon, Director, Health Financing and Public Health Issues, Health, Education, and Human Services Division, before the Special Committee on Ag. Washington, D.C: The Office, 1999.

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4

Office, General Accounting. Prescription drug benefits: Implications for beneficiaries of medicare HMO use of formularies : report to Congressional Requesters. Washington, D.C. (P.O. Box 37050, Washington, D.C. 20013): U.S. General Accounting Office, 1999.

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5

Insurance, California Legislature Senate Committee on. Informational hearing on how can the state protect senior citizens from dwindling prescription drug benefits? Sacramento, Calif: Senate Publications, 2000.

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California. Legislature. Senate. Committee on Insurance. Informational hearing on how can the state protect senior citizens from dwindling prescription drug benefits? Sacramento, CA (1020 N Street, Sacramento): Additional copies, Senate Publications, 2000.

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7

California. Bureau of State Audits. Department of Health Services: Its drug management techniques are similar to those of health maintenance organizations. Sacramento, Calif: Bureau of State Audits, 1997.

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8

Incorporated, CCH, ed. Medicare part D: Medicare prescription drug benefit : January 28, 2005 : including CCH explanations and new and revised regulations from 42 C.F.R. parts 400, 403, 411, 417, and 423. Chicago: CCH, 2005.

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9

Office, General Accounting. Medicare+Choice: Plan withdrawals indicate difficulty of providing choice while achieving savings : report to Congressional Requesters. Washington, D.C. (P.O. Box 37050, Washington, D.C. 20013): The Office, 2000.

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10

Office, General Accounting. Medicare+choice: New standards could improve accuracy and usefulness of plan literature : report to Congressional requesters. Washington, D.C. (P.O. Box 37050, Washington, D.C. 20013): The Office, 1999.

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Частини книг з теми "Prescriptive maintenance":

1

Koukaras, Paraskevas, Asimina Dimara, Sergio Herrera, Niccolò Zangrando, Stelios Krinidis, Dimosthenis Ioannidis, Piero Fraternali, Christos Tjortjis, Christos-Nikolaos Anagnostopoulos, and Dimitrios Tzovaras. "Proactive Buildings: A Prescriptive Maintenance Approach." In IFIP Advances in Information and Communication Technology, 289–300. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08341-9_24.

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2

Dias Longhitano, Pedro, Khaoula Tidriri, Christophe Bérenguer, and Benjamin Echard. "Proposition of a Generic Decision Framework for Prescriptive Maintenance." In Lecture Notes in Mechanical Engineering, 263–73. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96794-9_24.

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3

Greasley, Andrew. "Chapter 14. Case Study: A Simulation of a Rail Carriage Maintenance Depot." In Simulating Business Processes for Descriptive, Predictive, and Prescriptive Analytics, 280–88. Berlin, Boston: De Gruyter, 2019. http://dx.doi.org/10.1515/9781547400690-014.

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4

Tham, Chen-Khong, and Naman Sharma. "Prescriptive Maintenance Using Markov Decision Process and GPU-Accelerated Edge Computing." In Mobile Edge Computing, 167–81. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69893-5_8.

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Mohd Adha, Mat Esa, Muhammad Masdi, and Husin Hilmi. "Prescriptive Analytics for Dynamic Risk-Based Naval Vessel Maintenance Decision-Making." In Lecture Notes in Mechanical Engineering, 345–59. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-5946-4_28.

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Ansari, Fazel, Robert Glawar, and Wilfried Sihn. "Prescriptive Maintenance of CPPS by Integrating Multimodal Data with Dynamic Bayesian Networks." In Machine Learning for Cyber Physical Systems, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-59084-3_1.

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7

Franzen, Julian, Jannis Stecken, Raphael Pfaff, and Bernd Kuhlenkötter. "Using the Digital Shadow for a Prescriptive Optimization of Maintenance and Operation." In Advances in Production, Logistics and Traffic, 265–76. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13535-5_19.

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Adha Mat Esa, Mohd, and Masdi Muhammad. "A Comprehensive Review of Prescriptive Analytics for Naval Vessels Risk-Based Maintenance Decision-Making." In Lecture Notes in Mechanical Engineering, 503–15. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1939-8_39.

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Nikolaev, Sergei, Sergei Belov, Mikhail Gusev, and Ighor Uzhinsky. "Hybrid Data-Driven and Physics-Based Modelling for Prescriptive Maintenance of Gas-Turbine Power Plant." In IFIP Advances in Information and Communication Technology, 379–88. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-42250-9_36.

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Garrone, Andrea, Simone Minisi, Luca Oneto, Carlo Dambra, Marco Borinato, Paolo Sanetti, Giulia Vignola, Federico Papa, Nadia Mazzino, and Davide Anguita. "Simple Non Regressive Informed Machine Learning Model for Prescriptive Maintenance of Track Circuits in a Subway Environment." In Lecture Notes in Networks and Systems, 74–83. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16281-7_8.

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Тези доповідей конференцій з теми "Prescriptive maintenance":

1

Choubey, Suresh, Ryan Benton, and Tom Johnsten. "Prescriptive Equipment Maintenance: A Framework." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006213.

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Kothona, Despoina, Ioannis P. Panapakidis, and Georgios C. Christoforidis. "Prescriptive maintenance technique for photovoltaic systems." In 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). IEEE, 2022. http://dx.doi.org/10.1109/eeeic/icpseurope54979.2022.9854790.

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Zhao, Yang, Yajuan Sun, Linshan Jiang, and Ryan Chan Wei Yang. "Few-Shot Learning for Prescriptive Maintenance." In International Conference of Asian Society for Precision Engineering and Nanotechnology. Singapore: Research Publishing Services, 2022. http://dx.doi.org/10.3850/978-981-18-6021-8_or-08-0094.html.

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Chimundu, John, and Ahmed Ali. "Modern Maintenance Strategies Prescriptive Approach Using Machine learning." In 2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2022. http://dx.doi.org/10.1109/ei256261.2022.10116146.

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Marques, Henrique, and Alessandro Giacotto. "Prescriptive Maintenance: Building Alternative Plans for Smart Operations." In The 10th Aerospace Technology Congress, October 8-9, 2019, Stockholm, Sweden. Linköping University Electronic Press, 2019. http://dx.doi.org/10.3384/ecp19162027.

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Tham, Chen-Khong, Weihao Liu, and Rajarshi Chattopadhyay. "Prescriptive Maintenance of Freight Vehicles using Deep Reinforcement Learning." In 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring). IEEE, 2023. http://dx.doi.org/10.1109/vtc2023-spring57618.2023.10199753.

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Esposito, Nicola, Bruno Castanier, and Massimiliano Giorgiov. "A Prescriptive Maintenance Policy for a Gamma Deteriorating Unit." In 32nd European Safety and Reliability Conference. Singapore: Research Publishing Services, 2022. http://dx.doi.org/10.3850/978-981-18-5183-4_r14-05-139-cd.

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Esposito, Nicola, Bruno Castanier, and Massimiliano Giorgiov. "A Prescriptive Maintenance Policy for a Gamma Deteriorating Unit." In 32nd European Safety and Reliability Conference. Singapore: Research Publishing Services, 2022. http://dx.doi.org/10.3850/978-981-18-5183-4_r14-05-139.

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Strack, Bernhard, Jana Frank, Volker Stich, Martin Lenart, and Florian Pfau. "Sociotechnical Implementation of Prescriptive Maintenance for Onshore Wind Turbines." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2022. http://dx.doi.org/10.24251/hicss.2022.158.

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Esposito, Nicola, Bruno Castanier, and Massimiliano Giorgio. "An Adaptive Prescriptive Maintenance Policy for a Gamma Deteriorating Unit." In 33rd European Safety and Reliability Conference. Singapore: Research Publishing Services, 2023. http://dx.doi.org/10.3850/978-981-18-8071-1_p311-cd.

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Звіти організацій з теми "Prescriptive maintenance":

1

Soares, Tatiana Fontes, Alexis Smith-Juvelis, Cheryl Gray, and Alejandro Soriano. IDB-9: Financial and Risk Management. Inter-American Development Bank, March 2013. http://dx.doi.org/10.18235/0010520.

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Анотація:
This paper analyzes whether the Inter-American Development Bank (IDB, or Bank) has fully and effectively implemented the IDB-9 requirements related to risk and financial management. IDB-9 included four requirements in this area: (i) adopt a rule-based Income Management Model (IMM); (ii) implement the recently introduced risk-based Capital Adequacy Policy; (iii) execute a set of agreed actions to enhance the short-term sustainability of the Fund for Special Operations (FSO); and (iv) continue strengthening the Banks Risk Management Framework. The Bank has fully implemented the IDB-9 financial and risk management actions. The highly detailed and prescriptive nature of the requirements aided implementation. In terms of effectiveness, the IMM imposes financial discipline and enhances financial selfsustainability by linking Bank expenses directly to income through loan charges. The CAP supports prudent risk management and the Banks AAA rating. The actions taken for the FSO will not be sufficient to ensure the Funds sustainability until 2020, as mandated in IDB-9, and Management is preparing to propose additional measures for the Board¿s approval. A few issues with the IMM and CAP merit further review going forward. First, the IMM is very strict, with a high administrative expense coverage rule and its inclusion of nonoperational expenses. Second, it is not clear that the reserve ratios for sovereignguaranteed (SG) and non-sovereign-guaranteed (NSG) exposure adequately reflect their relative levels of risk or lead to the most effective leveraging of scarce Bank capital. Third, the Bank¿s unused borrowing capacity rule¿though perhaps reassuring to potential investors¿is outdated and is not relevant to the maintenance of the Bank¿s AAA rating, while a criterion that rating agencies do consider relevant¿country portfolio concentration¿is not factored into the Bank¿s rules. Finally, the IDB-9 architecture is inward-looking and does not promote a focus on the Bank¿s financial competitiveness. In light of these findings, OVE suggests that the Bank (i) consider introducing greater flexibility in the IMM by setting an administrative coverage band and perhaps excluding certain nonoperational expenses; (ii) review the capital accumulation rule and the reserve ratios for SG and NSG exposures; (iii) update the Bank¿s financial rules by phasing out the borrowing authority limit; and (iv) use the financial and risk management architecture as input to strategic decision-making on the projected size of the Bank, the blend of SG and NSG lending, expected countercyclical support, the role of the FSO, and the Bank¿s approach to future capitalization.

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