Articoli di riviste: "Prescriptive maintenance"

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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.

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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 (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.

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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 (2001): 195–209. http://dx.doi.org/10.1080/10803548.2001.11076486.

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Souza, Josiely Borges de, and Sandra Mara Santana Rocha. "PRESCRIPTIVE MAINTENANCE SYNCHRONIZED TO THE CHARACTERISTICS OF INDUSTRY 5.0: A THEORETICAL DISCUSSION." Journal of Technology and Operations Management 19, no. 1 (2024): 40–58. http://dx.doi.org/10.32890/jtom2024.19.1.4.

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This article aims to examine and understand the current scope of Prescriptive Maintenance integrated with the human-centric characteristics of Industry 5.0. Therefore, the main research question is the following: “In the view of the managers of the companies visited, located in the Southeast Region of Brazil, what are the main technical and administrative difficulties that companies encounter in successfully implementing proactive combinations on the factory floor, such as Prescriptive Maintenance integrated with the characteristics of Industry 5.0”?. The evolution of industrial processes in recent decades and the advent of Industry 4.0 integrated with human-centered characteristics have placed maintenance as one of the protagonists of industrial processes. In a scenario where automation and humans are gaining more and more space, the future of industries lies in Prescriptive Maintenance. To achieve the objective of the article, professional experiences from participation in congresses, seminars and technical visits to several factories located in the Southeast Region of Brazil are used. In data collection, we explored documentary observation and bibliographic research in the Science Direct and Web of Science databases. The study focuses on research areas related to maintenance integration, Industry 4.0 (Techno centric) and human-centric characteristics (Industry 5.0). The article brought important conclusions, as it addresses the impact and trends on the factory floor so that managers can develop competitiveness and productivity. The article highlights the role of maintenance methods in improving factory floor performance, emphasizing human knowledge and Prescriptive Maintenance practices to ensure operations within World Class standards.

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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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Setyobudi, Bima Bagus. "Strategi Pemeliharaan Preskriptif: Optimalisasi Keandalan Mesin Berbasis Machine Learning Guna Mencegah Terjadinya Downtime Pada Mesin Industri." Prosiding Seminar Nasional KONSTELASI 2, no. 1 (2025): 414–26. https://doi.org/10.24002/prosidingkonstelasi.v2i1.11199.

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Abstrak. Downtime pada mesin industri dapat menyebabkan kerugian yang signifikan dalam produktivitas dan efisiensi operasional. Berbagai metode pemeliharaan seperti Corrective, Descriptive, Diagnostic, dan Predictive Maintenance memiliki keterbatasan dalam mengoptimalkan strategi mitigasi downtime. Oleh karena itu, penelitian ini mengimplementasikan Prescriptive Maintenance berbasis Machine Learning (XGBoost) untuk tidak hanya memprediksi kegagalan mesin tetapi juga memberikan rekomendasi langkah korektif guna mencapai zero downtime. Dataset AI4I 2020 Predictive Maintenance digunakan sebagai sumber data, dengan menerapkan berbagai teknik preprocessing, seperti Min-Max Scaling, SMOTE, Heatmap Korelasi, VIF, serta deteksi outlier menggunakan Z-Score dan IQR. Model XGBoost dilatih untuk memprediksi probabilitas kegagalan mesin, yang kemudian dianalisis menggunakan Feature Importance untuk mengidentifikasi penyebab utama kegagalan. Evaluasi model menunjukkan akurasi 98.12%, precision 97.95%, recall 98.23%, dan AUC-Score 99.59%, membuktikan keandalan sistem dalam mendeteksi dan mengklasifikasikan kegagalan mesin. Dengan implementasi strategi ini yang didukung oleh IoT dan database real-time, industri dapat mengoptimalkan efisiensi pemeliharaan, mengurangi downtime tak terduga, serta meningkatkan keandalan operasional. Kata Kunci: Prescriptive Maintenance; Machine Learning; XGBoost; Zero Downtime. Abstract. Downtime in industrial machinery can cause significant losses in productivity and operational efficiency. Various maintenance methods such as Corrective, Descriptive, Diagnostic, and Predictive Maintenance have limitations in optimizing downtime mitigation strategies. Therefore, this study implements Prescriptive Maintenance using Machine Learning (XGBoost) to not only predict machine failures but also provide corrective recommendations to achieve zero downtime. The AI4I 2020 Predictive Maintenance Dataset is utilized, incorporating several preprocessing techniques, including Min-Max Scaling, SMOTE, Correlation Heatmap, VIF, and outlier detection using Z-Score and IQR. The XGBoost model is trained to predict the probability of machine failure, which is further analyzed using Feature Importance to identify the root cause of failures. Model evaluation results demonstrate 98.12% accuracy, 97.95% precision, 98.23% recall, and a 99.59% AUC-Score, proving the system’s reliability in detecting and classifying machine failures. With the implementation of this strategy, supported by IoT and real-time databases, industries can optimize maintenance efficiency, reduce unexpected downtime, and enhance operational reliability. Keywords: Prescriptive Maintenance; Machine Learning; XGBoost; Zero Downtime.

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Pop-Suărășan, Ana-Diana, Nicolae Stelian Ungureanu, and Adrian Petrovan. "Analytical Research Regarding the Implementation of a Prescriptive Maintenance System Applied to IT&C Equipment." Machines 13, no. 5 (2025): 412. https://doi.org/10.3390/machines13050412.

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In the era of digitalization, prescriptive maintenance and equipment condition monitoring are essential for ensuring the continuity of operations in various industries. The proposed software solution provides an integrated solution for monitoring and maintaining equipment, facilitating the collection, processing and interpretation of data related to their performance. Prioritization of prescriptive maintenance recommendations for alerts within the application is based on a set of well-defined criteria that integrate both the analysis of physical sensor data and computer logs. This is achieved through a hierarchical classification mechanism, combining predefined thresholds, prescriptive assessments and severity levels. The proposed project is a prescriptive equipment monitoring and maintenance application focused on the collection and analysis of sensor data to identify and prevent potential failures. The application structure integrates components such as data processing, machine learning, databases and recommendation generation, each of which has a specific role in the project workflow. The integration of sensor data and logs enhances robustness, scalability and efficiency, ensuring both high performance and adaptability to the varying needs of users. The proposed system processes and analyzes large data volumes. It combines threshold-based logic with machine learning to enhance adaptability.

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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 (2000): 6–178. http://dx.doi.org/10.1177/154193120004403327.

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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 (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.

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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 (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.

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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 (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.

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Shohet, Igal M., and Ad Straub. "PERFORMANCE-BASED-MAINTENANCE: A COMPARATIVE STUDY BETWEEN THE NETHERLANDS AND ISRAEL." International Journal of Strategic Property Management 17, no. 2 (2013): 199–209. http://dx.doi.org/10.3846/1648715x.2013.807482.

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Traditional maintenance contracts using the unit price system and prescriptive specifications are simple in their structure and implementation. Implementation of prescriptive-based specification reduces though the flexibility of the procurement and limits the possibilities of the contractor to improve the in-sight operation. Furthermore, the management of the contract faces difficulties such as poor performance of the buildings and ineffective contract management. Performance-Based-Maintenance (PBM) attains an alternative means for outsourcing of maintenance. The objective of the study was to comparatively assess the state-of-the-art of PBM between the Netherlands and Israel, with the focus on performance of public facilities and cost of the service. Pilot studies, carried out in the Netherlands and Israel, reveal that PBM attains high potential of cost-savings (20%) and improved performance. The paper concludes with provision of a future bidding system for PBM contracts.

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Frawley, Jack. "What Should Schools do About Aboriginal Language Maintenance?" Aboriginal Child at School 20, no. 1 (1992): 3–14. http://dx.doi.org/10.1017/s0310582200007690.

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In addressing this question, I will briefly outline the existing types of language maintenance program models, and what schools can do about Aboriginal language maintenance in the Aboriginal community school context. I have suggested a number of guiding principles for Aboriginal schools which are not intended to be prescriptive, but rather to establish a basis for dialogue and discussion from which language maintenance programs could be developed.

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Di Pasquale, Valentina, Salvatore Digiesi, Ivan Ferretti, and Antonio Padovano. "Building and sustaining competence in maintenance: a prescriptive training model." IFAC-PapersOnLine 58, no. 8 (2024): 174–79. http://dx.doi.org/10.1016/j.ifacol.2024.08.116.

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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 (2019): 482–503. http://dx.doi.org/10.1080/0951192x.2019.1571236.

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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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Petroutsatou, K., and I. Ladopoulos. "Integrated Prescriptive Maintenance System (PREMSYS) for Construction Equipment Based on Productivity." IOP Conference Series: Materials Science and Engineering 1218, no. 1 (2022): 012006. http://dx.doi.org/10.1088/1757-899x/1218/1/012006.

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Abstract It is now imperative to create smart systems that prevent mechanical damage through timely preventive maintenance, particularly in construction projects with strict time schedules and budgets. Construction equipment is the largest capital investment for construction companies. Proper maintenance is of major importance for efficiency, productivity, minimization of equipment costs, and environmental management. The aim of this study is to propose an integrated smart system that will monitor the condition based on productivity of the equipment and will provide diagnostic data, helping to optimize the production process, achieve timely maintenance and increasing the expected “economic” life of the equipment. At the same time, it will positively provide the concept for sustainable or green construction with the minimization and elimination of harmful effects on the environment and on the human. The system will include sensors, placed on specific construction equipment components, and will collect measurements for their use and condition, through real-time data export. These data will then be sent using wireless networks to a main server. Extraction of performance measurements and machine learning (ML) data processing will determine when the equipment needs predictive maintenance and repair. Proper, timely and prescriptive maintenance of construction equipment will reduce their environmental footprint and any human harmful emissions, while saving energy during their operating phase and optimizing production processes through monitoring “dead” time.

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Carvalho, Johnderson Nogueira de, Felipe Rodrigues da Silva, and Erick Giovani Sperandio Nascimento. "Challenges of the Biopharmaceutical Industry in the Application of Prescriptive Maintenance in the Industry 4.0 Context: A Comprehensive Literature Review." Sensors 24, no. 22 (2024): 7163. http://dx.doi.org/10.3390/s24227163.

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The biopharmaceutical industry has specificities related to the optimization of its processes, the effectiveness of the maintenance of the productive park in the face of regulatory requirements. and current concepts of modern industry. Current research on the subject points to investments in the health area using the current tools and concepts of Industry 4.0 (I4.0) with the objective of a more assertive production, reduction of maintenance costs, reduction of operating risks, and minimization of equipment idle time. In this context, this study aims to characterize the current knowledge about the challenges of the biopharmaceutical industry in the application of prescriptive maintenance, which derives from predictive maintenance, in the context of I4.0. To achieve this, a systematic review of the literature was carried out in the scientific knowledge bases IEEE Xplore, Scopus, Web of Science, Science Direct, and Google Scholar, considering works such as Reviews, Article Research, and Conference Abstracts published between 2018 and 2023. The results obtained revealed that prescriptive maintenance offers opportunities for improvement in the production process, such as cost reduction and greater proximity to all actors in the areas of production, maintenance, quality, and management. The limitations presented in the literature include a reduced number of models, the lack of a clearer understanding of its construction, lack of applications directly linked to the biopharmaceutical industry, and lack of measurement of costs and implementation time of these models. There are significant advances in this area including the implementation of more elaborate algorithms used in artificial intelligence neural networks, the advancement of the use of decision support systems as well as the collection of data in a more structured and intelligent way. It is concluded that for the adoption of prescriptive maintenance in the pharmaceutical industry, issues such as the definition of data entry and analysis methods, interoperability between “shop floor” and corporate systems, as well as the integration of technologies existing in the world, must be considered for I4.0.

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Kolar, Davor, Dragutin Lisjak, Martin Curman, and Michał Pająk. "Condition Monitoring of Rotary Machinery Using Industrial IOT Framework." Tehnički glasnik 16, no. 3 (2022): 343–52. http://dx.doi.org/10.31803/tg-20220517173151.

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Modern maintenance strategies, such as predictive and prescriptive maintenance, which derived from the concept of Industry and Maintenance 4.0, involve the application of the Industrial Internet of Things (IIoT) to connect maintenance objects enabling data collection and analysis that can help make better decisions on maintenance activities. Data collection is the initial step and the foundation of any modern Predictive or Prescriptive maintenance strategy because it collects data that can then be analysed to provide useful information about the state of maintenance objects. Condition monitoring of rotary equipment is one of the most popular maintenance methods because it can distinguish machine state between multiple fault types. The topic of this paper is the presentation of an automated system for data collection, processing and interpretation of rotary equipment state that is based on IIoT framework consisting of an IIoT accelerometer, edge and fog devices, web API and database. Additionally, ISO 10816-1 guidance has been followed to develop module for evaluation of vibration severity. The collected data is also visualized in a dashboard in a near-real time and shown to maintenance engineering, which is crucial for pattern monitoring. The developed system was launched in laboratory conditions using rotating equipment failure simulator to test the logic of data collection and processing. A proposed system has shown that it is capable of automated periodic data collection and processing from remote places which is achieved using Node RED programming environment and MQTT communication protocol that enables reliable, lightweight, and secure data transmission.

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Matyas, Kurt, Tanja Nemeth, Klaudia Kovacs, and Robert Glawar. "A procedural approach for realizing prescriptive maintenance planning in manufacturing industries." CIRP Annals 66, no. 1 (2017): 461–64. http://dx.doi.org/10.1016/j.cirp.2017.04.007.

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Grijalvo Martín, Mercedes, Antonia Pacios Álvarez, Joaquín Ordieres-Meré, Javier Villalba-Díez, and Gustavo Morales-Alonso. "New Business Models from Prescriptive Maintenance Strategies Aligned with Sustainable Development Goals." Sustainability 13, no. 1 (2020): 216. http://dx.doi.org/10.3390/su13010216.

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The industry has entered on the Fourth Industrial Revolution, the so-called Industry 4.0, with global markets and strong competition, some traditional manufacturing firms are implementing new maintenance innovations and policies, based on digitalisation and data driven approach, but also based on servitisation. The implementation of these new equipment maintenance business models, could require new organisational approach at different levels. Different sorts of integration are arranged, in vertical with a flat structure of intelligent, flexible and autonomous units working integrated, in horizontal with a strong external and internal supply chain integration, and in transverse, with an integrated approach that link internal and external stakeholders. A new prescriptive maintenance business model for equipment exploiting digitalisation opportunities, including stakeholder relationship is proposed. Different perspectives such as organisational, innovation and sustainability have been adopted to discuss the implications of the proposal. The social value potentially gained as well as the alignment with the SDGs are discussed as well.

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Gordon, Christopher Ampofo Kwadwo, Baris Burnak, Melis Onel, and Efstratios N. Pistikopoulos. "Data-Driven Prescriptive Maintenance: Failure Prediction Using Ensemble Support Vector Classification for Optimal Process and Maintenance Scheduling." Industrial & Engineering Chemistry Research 59, no. 44 (2020): 19607–22. http://dx.doi.org/10.1021/acs.iecr.0c03241.

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Tolstow, K. T., A. Beiky, and I. El-Thalji. "Maintenance philosophy for an unmanned platform: A case study for an Offshore wind substation." IOP Conference Series: Materials Science and Engineering 1201, no. 1 (2021): 012085. http://dx.doi.org/10.1088/1757-899x/1201/1/012085.

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Abstract Unmanned installations introduce new challenges for the operations and maintenance. In many offshore windfarms there is a need for offshore substations and these substations can be designed as unmanned installations. Thus, the purpose of this paper is to review and discuss the available maintenance philosophies for unmanned substation platforms and as a result, propose a suitable maintenance philosophy for this type of installations with regards to key performance indicators such as high availability, low maintenance man-hours and minimal fixed maintenance campaigns. It is concluded that maintenance philosophy for unmanned substation shall consider the DIP (Design out and Intelligent preventive maintenance) concept, where the potential benefits of risk-based maintenance, design-out, predictive, and prescriptive maintenance are unlocked. To illustrate the utilization of proposed philosophy, a case study related to cooling system of a substation is presented

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Nordal, Helge, and Idriss El-Thalji. "Lifetime Benefit Analysis of Intelligent Maintenance: Simulation Modeling Approach and Industrial Case Study." Applied Sciences 11, no. 8 (2021): 3487. http://dx.doi.org/10.3390/app11083487.

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The introduction of Industry 4.0 is expected to revolutionize current maintenance practices by reaching new levels of predictive (detection, diagnosis, and prognosis processes) and prescriptive maintenance analytics. In general, the new maintenance paradigms (predictive and prescriptive) are often difficult to justify because of their multiple inherent trade-offs and hidden systems causalities. The prediction models, in the literature, can be considered as a “black box” that is missing the links between input data, analysis, and final predictions, which makes the industrial adaptability to such models almost impossible. It is also missing enable modeling deterioration based on loading, or considering technical specifications related to detection, diagnosis, and prognosis, which are all decisive for intelligent maintenance purposes. The purpose and scientific contribution of this paper is to present a novel simulation model that enables estimating the lifetime benefits of an industrial asset when an intelligent maintenance management system is utilized as mixed maintenance strategies and the predictive maintenance (PdM) is leveraged into opportunistic intervals. The multi-method simulation modeling approach combining agent-based modeling with system dynamics is applied with a purposefully selected case study to conceptualize and validate the simulation model. Three maintenance strategies (preventive, corrective, and intelligent) and five different scenarios (case study data, manipulated case study data, offshore and onshore reliability data handbook (OREDA) database, physics-based data, and hybrid) are modeled and simulated for a time period of 20 years (175,200 h). Intelligent maintenance is defined as PdM leveraged in opportunistic maintenance intervals. The results clearly demonstrate the possible lifetime benefits of implementing an intelligent maintenance system into the case study as it enhanced the operational availability by 0.268% and reduced corrective maintenance workload by 459 h or 11%. The multi-method simulation model leverages and shows the effect of the physics-based data (deterioration curves), loading profiles, and detection and prediction levels. It is concluded that implementing intelligent maintenance without an effective predictive horizon of the associated PdM and effective frequency of opportunistic maintenance intervals, does not guarantee the gain of its lifetime benefits. Moreover, the case study maintenance data shall be collected in a complete (no missing data) and more accurate manner (use hours instead of date only) and used to continuously upgrade the failure rates and maintenance times.

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Moros, D., N. Berrabah, K. D. Searle, and I. G. Ashton. "Maintenance & failure data analysis of an offshore wind farm." Journal of Physics: Conference Series 2767, no. 6 (2024): 062006. http://dx.doi.org/10.1088/1742-6596/2767/6/062006.

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Abstract Offshore Wind (OW) continues to grow globally at a rapid pace, with growth estimates of 630GW by 2050. To facilitate this rapid growth, costs must continue to be reduced. Reducing operations and maintenance (O&M) costs, which are estimated at 30% of the lifetime costs of wind farms, offers opportunity. This could be achieved by moving current maintenance strategies to a prescriptive strategy. Prescriptive strategies use the turbine monitoring data to determine component remaining useful lifetimes or predict failure windows and then provide an optimised maintenance plan. The first stage of a framework, that can be applied to operational assets, for improving maintenance schedules with failure predictions is presented. Analysis of the SCADA system and the maintenance logs, at an operational offshore wind farm (OWF), with the purpose of identifying turbine failure rates, availabilities and losses and costs from maintenance and failures has been performed. The analysis has revealed two types of maintenance actions, one is cost of maintenance driven and the other cost of downtime driven. It is proposed that, given different characteristics, they should be approached differently in the context of failure predictions. It is also revealed that electrical components are critical to the failure rate and energy losses due to maintenance at the OWF. Electrical components represent approximately 28% of all failures and nearly 40% of revenue loss due downtimes from failure for the period analysed. The power converters drive most electrical failures and are of key commercial interest to the farm. As a result, the power converters should be the target for future prognostic model development. The analysis also shows that with perfect prediction and maintenance scheduling, this OWF could generate an extra 0.26% revenue and a generic 1GW OWF could generate an extra 0.6% extra energy and approximately £1.4m in revenue. This analysis did not reveal the benefit of taking fewer maintenance actions, which should be assessed in future work. Producing a combined prognostic maintenance scheduling method will generate extra wind farm revenues, reduce the number of maintenance actions taken and facilitate the work of maintenance teams.

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Gonzaga, Luiz Gonzaga da Costa Neto. "analysis of the possibility of reducing spare parts in the maintenance context 4.0." Independent Journal of Management & Production 13, no. 1 (2022): 350–63. http://dx.doi.org/10.14807/ijmp.v13i1.1550.

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Equipment inspection arose from the need to maintain industrial facilities in satisfactory physical condition, providing the minimum level of safety and reliability in operation and maintenance. Machines and equipment cannot stop operating without planning and managing the supply chain, maintaining large quantities of spare parts stocks. The objective of this work is to analyze the possibility of predictive and / or prescriptive monitoring to contribute to the preventive maintenance of the real condition of the equipment, that is, only in real need, avoiding preventive maintenance for pre-established intervals, consequently reducing the stock of spare parts. To this end, a Systematic Literature Review (RSL) was carried out using the keywords Maintenance 4.0 and Supply Chain Management and Industrial Logistics, identifying the main authors and most relevant journals.

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Costa, J., José Torres Farinha, Mateus Mendes, and J. O. Estima. "AI-Driven Belt Failure Prediction and Prescriptive Maintenance with Motor Current Signature Analysis." Applied Sciences 15, no. 12 (2025): 6947. https://doi.org/10.3390/app15126947.

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Industrial belt failures pose significant challenges to manufacturing operations, often resulting in costly downtime and maintenance interventions. This study presents a comprehensive approach to belt failure analysis, leveraging advanced monitoring and diagnostic techniques. Through the integration of motor current signature analysis (MCSA) and machine learning algorithms, particularly long short-term memory (LSTM) networks, this study aims to predict and detect belt degradation in real time. The methodology involves the collection and pre-processing of raw spectral data from industrial assets, followed by the training and optimization of predictive models. The effectiveness of the approach is demonstrated through extensive testing against real-world data, showcasing its ability to accurately forecast belt failures and enable proactive maintenance strategies. The results obtained from the testing phase reveal a high level of accuracy in predicting belt failures, with the developed models consistently outperforming traditional methods. The incorporation of LSTM networks and swarm intelligence algorithms led to a significant improvement in predictive capabilities, allowing for the early detection of degradation patterns and timely intervention. By harnessing the power of data-driven predictive analytics, the research offers a promising pathway towards enhancing operational efficiency and minimizing unplanned downtime in industrial settings. This study not only contributes to the field of predictive maintenance but also underscores the transformative potential of advanced monitoring technologies in optimizing asset reliability and performance.

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Wesendrup, Kevin, Bernd Hellingrath, and Zoi Nikolarakis. "A framework for conceptualizing integrated prescriptive maintenance and production planning and control models." Brazilian Journal of Operations & Production Management 21, no. 3 (2024): 2172. http://dx.doi.org/10.14488/bjopm.2172.2024.

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Goal: This study aims to support researchers and managers in conceptualising new integrated prescriptive maintenance (PxM) and production planning and control (PPC) models. Design / Methodology / Approach: We perform a systematic literature review based on Thomé et al. (2016) and analyse literary findings using qualitative content analysis and quantitative correlation analyses. Results: This work identifies 94 integrated PxM and PPC planning models and 47 outcomes, 16 decision variables and 34 environment entities. Based on the quantitative analyses of these components, we derive a normative framework to guide researchers and practitioners in conceptualising integrated models. Limitations of the investigation: The study is limited to only one scientific database. Additionally, the quantitative analyses might be sensitive due to a low sample size for some components, and we only measure the linear dependency between two components. Lastly, we do not address solution algorithms. Practical implications: The framework constitutes a tool for managers to construct integrated models tailored to their specific planning problems, fostering alignment between production and maintenance departments, plans and controls. Originality / Value: We provide a descriptive overview and normative guidance in the selection of components that can or should be used for future PxM-aligned PPC planning studies, pinpointing possible research gaps.

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Biebl, Fabian, Robert Glawar, Anahid Jalali, et al. "A conceptual model to enable prescriptive maintenance for etching equipment in semiconductor manufacturing." Procedia CIRP 88 (2020): 64–69. http://dx.doi.org/10.1016/j.procir.2020.05.012.

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Jeon, Chi-Ho, Chang-Su Shim, Yang-Hee Lee, and Jennifer Schooling. "Prescriptive maintenance of prestressed concrete bridges considering digital twin and key performance indicator." Engineering Structures 302 (March 2024): 117383. http://dx.doi.org/10.1016/j.engstruct.2023.117383.

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Wesendrup, Kevin, Mergim Mustafa, and Bernd Hellingrath. "Degradation-agnostic integrated prescriptive maintenance and production scheduling simulation for electrophoretic dip coating system." Procedia CIRP 130 (2024): 1210–15. http://dx.doi.org/10.1016/j.procir.2024.10.229.

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Ordieres-Meré, Joaquín, Antonio Sánchez-Herguedas, and Ángel Mena-Nieto. "A Data-Driven Monitoring System for a Prescriptive Maintenance Approach: Supporting Reinforcement Learning Strategies." Applied Sciences 15, no. 12 (2025): 6917. https://doi.org/10.3390/app15126917.

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The aim of this study was to evaluate machine learning algorithms’ capacity to improve prescriptive maintenance. A pumping system consisting of two hydraulic pumps with an electric motor from a Spanish petrochemical company was used as a case study. Sensors were used to record data on the variables, with the target variable being the bearing temperature of the electric motor. Several regression models and a neural network time series model were tested to model the system variables. A bearing temperature sensitivity analysis was conducted based on the coefficients obtained from the optimization of the regression model. To fully exploit the capabilities of these techniques for application in this field, we designed a reference framework intended to foster model deployment in an industrial context by promoting the self-monitoring and updating of the models when required. The impact on decision-making processes is explored using reinforcement learning in the context of this framework.

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Rahman, Azwanizam b. Che Abdul, Mohd Rahmat Tahir, M. Hamdan Kamarul Bahraini, Mohd Helmi Halim, and Muzamir Isa. "Digital Tools Approach Based On ‘Gerun’ And Online Partial Discharge Monitoring Project To Resolve Alternator’s Failure." Journal of Physics: Conference Series 2312, no. 1 (2022): 012041. http://dx.doi.org/10.1088/1742-6596/2312/1/012041.

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Abstract There is no online monitoring system and digital tools to monitor generator gas turbine healthiness led to generator failure caused un-planned outages resulted power import and penalty chargers. Maintenance program for generator is based on time-based method. Since 2016 to 2020, Asset Team spent millions of ringgit Malaysia for generator rectification and incurred power import cost due to the failure. To overcome the tripping issues, Generator Health Monitoring System was initiated as a research/pilot project as tools to monitor healthiness of Gas Turbine Generator. This project was categorized as digital transformation initiative (Revolution 4.0) as to enable online monitoring & performing diagnostic & prescriptive. The initiative consisted of 2 main pillars which are Generator Rotor Unhealthiness Notification (GERUN) and Online Partial Discharge Monitoring System (PD ONLINE). The aim of this paper is to share the experience in monitoring, economical way, diagnosis, condition evaluation, and possibility of predicting the performance of the generator. The methodology of the Generator Healthiness is to monitor the real time performance of the generator during running condition through rotor thermal losses, ratio of excitation current over the real output power and stator partial discharge pattern. This real time monitoring can improve the maintenance strategy and planning. As a result, plant’s OEE can be increased, reduce the MTBF and more importantly the repair cost. The parameters have been introduced to prevent the generator sudden failure event and bridge the gap condition monitoring especially on the generator part. Arriving to the objective, this project was categorized as digital transformation initiative (Revolution 4.0) as to enable online monitoring & performing diagnostic & prescriptive. The initiative consisted of 2 main pillars which are Generator Rotor Unhealthiness Notification (GERUN) and Online Partial Discharge monitoring system (PD ONLINE) project. Diagnose & prescriptive and provide asset owners with insights on alternator’s health, subsequently prevent unforeseen failure in real time and prescribe operational mitigations to prolong alternator’s life. Recommends targeted test and maintenance (3rd level maintenance predictive) addressing the exact health issue of the alternator value creation & cost saving by eliminating tripping, scope reduction for major inspection, non-dependable on FSR, internal troubleshooting and etc. The system has been implemented across gas processing and utilities Petronas Gas Berhad for 14 units of gas turbine generator. In view of the success implementation, this digital tool will be duplicate to others Petronas Operating Unit.

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Shriprakashan. L. Parapalli and Jithen Shetty. "Leveraging Hybrid Edge-Cloud Predictive Maintenance in Pharmaceutical MES: An Industry 4.0 Approach Using Big Data." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 86–94. https://doi.org/10.32996/jcsts.2025.7.2.7.

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The pharmaceutical sector relies on stringent manufacturing environments to safeguard product integrity and uphold regulatory standards. Unexpected equipment failures can lead to costly downtime, regulatory exposure, and compromised quality. To address these challenges, this paper presents an integrated Hybrid Edge-Cloud Predictive Maintenance (HEC-PdM) framework embedded within a Manufacturing Execution System (MES). By combining edge computing for real-time anomaly detection with cloud-based machine learning (ML) analytics, manufacturers can transition from reactive to predictive and prescriptive maintenance strategies. The methodology includes data collection and preprocessing at the edge, federated learning in the cloud, and seamless MES integration to automate maintenance workflows and compliance documentation. Case studies highlight significant benefits, such as a 45% reduction in maintenance costs, minimized downtime, and improved production quality. Finally, the paper discusses future directions, including enhanced security protocols for federated learning, self-adaptive AI systems, and quantum ML to further address the complexities of pharmaceutical manufacturing.

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Swarun Kumar Joginpelly. "Predictive Analytics for Equipment Failure: Implementation and Outcomes of the Equipment Predisposed Tool." Journal of Computer Science and Technology Studies 7, no. 4 (2025): 240–47. https://doi.org/10.32996/jcsts.2025.7.4.29.

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This article presents a comprehensive investigation of the Equipment Predisposed Tool (EPT), a predictive analytics system designed to forecast equipment failures in industrial environments. It examines how advanced data analytics and machine learning techniques can transform maintenance operations from reactive to proactive paradigms. It details the data infrastructure, analytical methods, system architecture, and implementation strategies that underpin successful predictive maintenance initiatives. The article analyzes how the integration of sensor data, operational metrics, maintenance records, and environmental information can provide early detection of potential failures. It further explores various analytical techniques including time series analysis, machine learning classification, and reliability engineering models that collectively enable accurate prediction. Additionally, the analysis documents the operational benefits, financial returns, and key insights gained from the implementation while identifying future development directions in areas such as deep learning, digital twins, and prescriptive analytics.

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Papaioannou, Alexios, Asimina Dimara, Charalampos S. Kouzinopoulos, et al. "LP-OPTIMA: A Framework for Prescriptive Maintenance and Optimization of IoT Resources for Low-Power Embedded Systems." Sensors 24, no. 7 (2024): 2125. http://dx.doi.org/10.3390/s24072125.

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Low-power embedded systems have been widely used in a variety of applications, allowing devices to efficiently collect and exchange data while minimizing energy consumption. However, the lack of extensive maintenance procedures designed specifically for low-power systems, coupled with constraints on anticipating faults and monitoring capacities, presents notable difficulties and intricacies in identifying failures and customized reaction mechanisms. The proposed approach seeks to address the gaps in current resource management frameworks and maintenance protocols for low-power embedded systems. Furthermore, this paper offers a trilateral framework that provides periodic prescriptions to stakeholders, a periodic control mechanism for automated actions and messages to prevent breakdowns, and a backup AI malfunction detection module to prevent the system from accessing any stress points. To evaluate the AI malfunction detection module approach, three novel autonomous embedded systems based on different ARM Cortex cores have been specifically designed and developed. Real-life results obtained from the testing of the proposed AI malfunction detection module in the developed embedded systems demonstrated outstanding performance, with metrics consistently exceeding 98%. This affirms the efficacy and reliability of the developed approach in enhancing the fault tolerance and maintenance capabilities of low-power embedded systems.

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Papaioannou, Alexios, Asimina Dimara, Charalampos Kouzinopoulos, et al. "LP-OPTIMA: A Framework for Prescriptive Maintenance and Optimization of IoT Resources for Low-Power Embedded Systems." Sensors 24 (March 26, 2024): 2125. https://doi.org/10.3390/s24072125.

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Low-power embedded systems have been widely used in a variety of applications, allowing devices to efficiently collect and exchange data while minimizing energy consumption. However, the lack of extensive maintenance procedures designed specifically for low-power systems, coupled with constraints on anticipating faults and monitoring capacities, presents notable difficulties and intricacies in identifying failures and customized reaction mechanisms. The proposed approach seeks to address the gaps in current resource management frameworks and maintenance protocols for low-power embedded systems. Furthermore, this paper offers a trilateral framework that provides periodic prescriptions to stakeholders, a periodic control mechanism for automated actions and messages to prevent breakdowns, and a backup AI malfunction detection module to prevent the system from accessing any stress points. To evaluate the AI malfunction detection module approach, three novel autonomous embedded systems based on different ARM Cortex cores have been specifically designed and developed. Real-life results obtained from the testing of the proposed AI malfunction detection module in the developed embedded systems demonstrated outstanding performance, with metrics consistently exceeding 98%. This affirms the efficacy and reliability of the developed approach in enhancing the fault tolerance and maintenance capabilities of low-power embedded systems.

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Molęda, Marek, Bożena Małysiak-Mrozek, Weiping Ding, Vaidy Sunderam, and Dariusz Mrozek. "From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry." Sensors 23, no. 13 (2023): 5970. http://dx.doi.org/10.3390/s23135970.

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Appropriate maintenance of industrial equipment keeps production systems in good health and ensures the stability of production processes. In specific production sectors, such as the electrical power industry, equipment failures are rare but may lead to high costs and substantial economic losses not only for the power plant but for consumers and the larger society. Therefore, the power production industry relies on a variety of approaches to maintenance tasks, ranging from traditional solutions and engineering know-how to smart, AI-based analytics to avoid potential downtimes. This review shows the evolution of maintenance approaches to support maintenance planning, equipment monitoring and supervision. We present older techniques traditionally used in maintenance tasks and those that rely on IT analytics to automate tasks and perform the inference process for failure detection. We analyze prognostics and health-management techniques in detail, including their requirements, advantages and limitations. The review focuses on the power-generation sector. However, some of the issues addressed are common to other industries. The article also presents concepts and solutions that utilize emerging technologies related to Industry 4.0, touching on prescriptive analysis, Big Data and the Internet of Things. The primary motivation and purpose of the article are to present the existing practices and classic methods used by engineers, as well as modern approaches drawing from Artificial Intelligence and the concept of Industry 4.0. The summary of existing practices and the state of the art in the area of predictive maintenance provides two benefits. On the one hand, it leads to improving processes by matching existing tools and methods. On the other hand, it shows researchers potential directions for further analysis and new developments.

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Molęda, Marek, Bozena Małysiak-Mrozek, Weiping Ding, Vaidy Sunderam, and Dariusz Mrozek. "From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry." Sensors 23, no. 13 (2023): 1–47. https://doi.org/10.3390/s23135970.

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Abstract (sommario):

Appropriate maintenance of industrial equipment keeps production systems in good health and ensures the stability of production processes. In specific production sectors, such as the electrical power industry, equipment failures are rare but may lead to high costs and substantial economic losses not only for the power plant but for consumers and the larger society. Therefore, the power production industry relies on a variety of approaches to maintenance tasks, ranging from traditional solutions and engineering know-how to smart, AI-based analytics to avoid potential downtimes. This review shows the evolution of maintenance approaches to support maintenance planning, equipment monitoring and supervision. We present older techniques traditionally used in maintenance tasks and those that rely on IT analytics to automate tasks and perform the inference process for failure detection. We analyze prognostics and health-management techniques in detail, including their requirements, advantages and limitations. The review focuses on the power-generation sector. However, some of the issues addressed are common to other industries. The article also presents concepts and solutions that utilize emerging technologies related to Industry 4.0, touching on prescriptive analysis, Big Data and the Internet of Things. The primary motivation and purpose of the article are to present the existing practices and classic methods used by engineers, as well as modern approaches drawing from Artificial Intelligence and the concept of Industry 4.0. The summary of existing practices and the state of the art in the area of predictive maintenance provides two benefits. On the one hand, it leads to improving processes by matching existing tools and methods. On the other hand, it shows researchers potential directions for further analysis and new developments.

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Gonçalves Rosado, Carlos Antônio, and Gilson Ricardo De Melo. "ANÁLISE DE ESTRATÉGIAS PARA DISPONIBILIDADE DE MATERIAIS EM MANUTENÇÃO DE MÁQUINAS DE VIA: UMA REVISÃO SISTEMÁTICA." RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218 5, no. 12 (2024): e5125978. https://doi.org/10.47820/recima21.v5i12.5978.

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This systematic research addresses the critical importance of material availability for the effective maintenance of track machines, a fundamental element to ensure safety and efficiency in railway operations. The central problem investigated focuses on the challenges faced in provisioning these materials, including logistical issues, stock errors, and delivery delays, which can compromise timely and efficient maintenance. The aim of this study is to analyze innovative and effective strategies that can optimize the provisioning of materials necessary for track machine maintenance. To achieve this goal, a bibliographic research methodology was adopted, focusing on the qualitative analysis of 19 literatures published between 2018 and 2023. The results reveal a convergence among authors on the need to integrate strategic planning, advanced technologies such as artificial intelligence, and efficient inventory management to overcome provisioning challenges. Prescriptive maintenance strategies and the use of advanced diagnostic technologies are highlighted as key elements for more proactive and less reactive maintenance. The final considerations demonstrate that the research problem has been thoroughly explored, with the proposed objectives being achieved. For future research, evaluating the real impact of emerging technologies on the life cycle of railway maintenance is suggested, aiming to provide effective strategies for the continuous optimization of maintenance operations.

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Finch, Byron J., and James P. Gilbert. "Developing maintenance craft labor efficiency through an integrated planning and control system: A prescriptive model." Journal of Operations Management 6, no. 3-4 (1986): 449–59. http://dx.doi.org/10.1016/0272-6963(86)90016-1.

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Gibey, Gaultier, Elodie Pahon, Noureddine Zerhouni, and Daniel Hissel. "Diagnostic and prognostic for prescriptive maintenance and control of PEMFC systems in an industrial framework." Journal of Power Sources 613 (September 2024): 234864. http://dx.doi.org/10.1016/j.jpowsour.2024.234864.

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DENG, HAOXUAN, Bernadin Namoano, BOHAO ZHENG, Samir Khan, and John Ahmet Erkoyuncu. "From Prediction to Prescription: Large Language Model Agent for Context-Aware Maintenance Decision Support." PHM Society European Conference 8, no. 1 (2024): 10. http://dx.doi.org/10.36001/phme.2024.v8i1.4114.

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Predictive analytics with machine learning approaches has widely penetrated and shown great success in system health management over the decade. However, how to convert the prediction to an actionable plan for maintenance is still far from mature. This study investigates how to narrow the gapbetween predictive outcomes and prescriptive descriptions for system maintenance using an agentic approach based on the large language model (LLM). Additionally, with the retrieval-augmented generation (RAG) technique and tool usage capability, the LLM can be context-aware when making decisions in maintenance strategy proposals considering predictions from machine learning. In this way, the proposed method can push forward the boundary of current machine-learning methods from a predictor to an advisor for decision-making workload offload. For verification, a case study on linear actuator fault diagnosis is conducted with the GPT-4 model. The result demonstrates that the proposed method can perform fault detection without extra training or fine-tuning with comparable performance to baseline methods and deliver more informatic diagnosis analysis and suggestions. This research can shed light on the application of large language models in the construction of versatile and flexible artificial intelligence agents for maintenance tasks.

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Ighravwe, Desmond Eseoghene. "Assessment of Sustainable Maintenance Strategy for Manufacturing Industry." Sustainability 14, no. 21 (2022): 13850. http://dx.doi.org/10.3390/su142113850.

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This study creates a framework to aid in the sustainability of maintenance strategies. The framework was created using expertise from the industry and academia. Using this knowledge, three multi-criteria tools were chosen for the maintenance strategies evaluation. The tools include grey relational analysis (GRA) techniques, additive ratio assessment (ARAS), and step-wise weight assessment ratio analysis (SWARA). In a production system, they were used to assess four planned maintenance strategies. The strategies are periodic maintenance (S1), meter-based maintenance (S2), predictive maintenance (S3) and prescriptive maintenance (S4). The ARAS approach was used to obtain the strategy rating for the various requirements. This study used the SWARA method to determine the requirements’ importance using an intuitionistic fuzzy triangular number. The ARAS results were combined using the GRA method. This study observed that the criteria utilised to choose a maintenance strategy for equipment depend on the information collected from six specialists in a manufacturing organisation. For instance, it was discovered that S3 was the maintenance approach that best suited the system’s technical needs. At the same time, S2 was found to be less effective. The economic needs analysis showed that S1 is the maintenance strategy that is most appropriate for the system, while S3 is the least appropriate. S1 is the most appropriate maintenance method for the system, given the social requirements, whereas S2 is the least effective. According to the results of the environmental requirements, S2 is the best maintenance plan for the system, while S4 is the worst. According to the GRA approach, the system’s best and least appropriate maintenance strategies are S2 and S4, respectively.

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Sathyapalan, Dipu T., Jini James, Sangita Sudhir, et al. "Antimicrobial Stewardship and Its Impact on the Changing Epidemiology of Polymyxin Use in a South Indian Healthcare Setting." Antibiotics 10, no. 5 (2021): 470. http://dx.doi.org/10.3390/antibiotics10050470.

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Polymyxins being last resort drugs to treat infections triggered by multidrug-resistant pathogens necessitates the implementation of antimicrobial stewardship program (ASP) initiatives to support its rational prescription across healthcare settings. Our study aims to describe the change in the epidemiology of polymyxins and patient outcomes following the implementation of ASP at our institution. The antimicrobial stewardship program initiated in February 2016 at our 1300 bed tertiary care center involved post-prescriptive audits tracking polymyxin consumption and evaluating prescription appropriateness in terms of the right indication, right frequency, right drug, right duration of therapy and administration of the right loading dose (LD) and maintenance dose (MD). Among the 2442 polymyxin prescriptions tracked over the entire study period ranging from February 2016 to January 2020, the number of prescriptions dropped from 772 prescriptions in the pre-implementation period to an average of 417 per year during the post-implementation period, recording a 45% reduction. The quarterly patient survival rates had a significant positive correlation with the quarterly prescription appropriateness rates (r = 0.4774, p = 0.02), right loading dose (r = 0.5228, p = 0.015) and right duration (r = 0.4361, p = 0.04). Our study on the epidemiology of polymyxin use demonstrated favorable effects on the appropriateness of prescriptions and mortality benefits after successful implementation of antimicrobial stewardship in a real-world setting.

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Santiago, Rogerio Adriano da Fonseca, Natasha Benjamim Barbosa, Henrique Gomes Mergulhão, et al. "Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis." Energies 17, no. 5 (2024): 1010. http://dx.doi.org/10.3390/en17051010.

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Wind energy has achieved a leading position among renewable energies. The global installed capacity in 2022 was 906 GW of power, with a growth of 8.4% compared to the same period in the previous year. The forecast is that the barrier of 1,000,000 MW of installed wind capacity in the world will be exceeded in July 2023, according to data from the World Association of Wind Energy. In order to support the expected growth in the wind sector, maintenance strategies for wind turbines must provide the reliability and availability necessary to achieve these goals. The usual maintenance procedures may present difficulties in keeping up with the expansion of this energy source. The objective of this work was to carry out a systematic review of the literature focused on research on the predictive and prescriptive maintenance of wind turbines based on the implementation of data-oriented models with the use of artificial intelligence tools. Deep machine learning models involving the detection, diagnosis, and prognosis of failures in this equipment were addressed.

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Tektaş, Berna, Hasan Hüseyin Turan, Nihat Kasap, Ferhan Çebi, and Dursun Delen. "A Fuzzy Prescriptive Analytics Approach to Power Generation Capacity Planning." Energies 15, no. 9 (2022): 3176. http://dx.doi.org/10.3390/en15093176.

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This study examines the long-term energy capacity investment problem of a power generation company (GenCo), considering the drought threat posed by climate change in hydropower resources in Turkey. The mid-term planning decisions such as maintenance and refurbishment scheduling of power plants are also considered in the studied investment planning problem. In the modeled electricity market, it is assumed that GenCos conduct business in uncertain market conditions with both bilateral contracts (BIC) and day-ahead market (DAM) transactions. The problem is modeled as a fuzzy mixed-integer linear programming model with a fuzzy objective and fuzzy constraints to handle the imprecisions regarding both the electricity market (e.g., prices) and environmental factors (e.g., hydroelectric output due to drought). Bellman and Zadeh’s max-min criteria are used to transform the fuzzy capacity investment model into a model with a crisp objective and constraints. The applicability of methodology is illustrated by a case study on the Turkish electric market in which GenCo tries to find the optimal power generation investment portfolio that contains five various generation technologies alternatives, namely, hydropower, wind, conventional and advanced combined-cycle natural gas, and steam (lignite) turbines. The results show that wind turbines with low marginal costs and steam turbines with high energy conversion efficiency are preferable, compared with hydroelectric power plant investments when the fuzziness in hydroelectric output exists (i.e., the expectation of increasing drought conditions as a result of climate change). Furthermore, the results indicate that the gas turbine investments were found to be the least preferable due to high gas prices in all scenarios.

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Padovano, Antonio, Francesco Longo, Letizia Nicoletti, Lucia Gazzaneo, Alessandro Chiurco, and Simone Talarico. "A prescriptive maintenance system for intelligent production planning and control in a smart cyber-physical production line." Procedia CIRP 104 (2021): 1819–24. http://dx.doi.org/10.1016/j.procir.2021.11.307.

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Nemeth, Tanja, Fazel Ansari, Wilfried Sihn, Bernhard Haslhofer, and Alexander Schindler. "PriMa-X: A reference model for realizing prescriptive maintenance and assessing its maturity enhanced by machine learning." Procedia CIRP 72 (2018): 1039–44. http://dx.doi.org/10.1016/j.procir.2018.03.280.

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Glawar, Robert, Fazel Ansari, Csaba Kardos, Kurt Matyas, and Wilfried Sihn. "Conceptual Design of an Integrated Autonomous Production Control Model in association with a Prescriptive Maintenance Model (PriMa)." Procedia CIRP 80 (2019): 482–87. http://dx.doi.org/10.1016/j.procir.2019.01.047.

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