Einloggen

Thematische Bibliographien / Data-driven maintenance / Zeitschriftenartikel

Um die anderen Arten von Veröffentlichungen zu diesem Thema anzuzeigen, folgen Sie diesem Link: Data-driven maintenance.

Zeitschriftenartikel zum Thema „Data-driven maintenance“

Autor: Grafiati

Veröffentlicht am 25. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an

Wählen Sie eine Art der Quelle aus:

Machen Sie sich mit Top-50 Zeitschriftenartikel für die Forschung zum Thema "Data-driven maintenance" bekannt.

Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.

Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.

Sehen Sie die Zeitschriftenartikel für verschiedene Spezialgebieten durch und erstellen Sie Ihre Bibliographie auf korrekte Weise.

1

Rios, Pablo. "Data-Driven Maintenance." Manufacturing Management 2023, no. 1-2 (2023): 32–33. http://dx.doi.org/10.12968/s2514-9768(23)90381-9.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

2

Afful-Dadzie, Anthony, and Theodore T. Allen. "Data-Driven Cyber-Vulnerability Maintenance Policies." Journal of Quality Technology 46, no. 3 (2014): 234–50. http://dx.doi.org/10.1080/00224065.2014.11917967.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

3

Ostrowski, João, and József Menyhárt. "Enhancing maintenance with a data-driven approach." International Review of Applied Sciences and Engineering 10, no. 2 (2019): 135–40. http://dx.doi.org/10.1556/1848.2019.0016.

Der volle Inhalt der Quelle

Annotation:

Constant stream of data has been generated and stored as more devices are being connected to the internet and supported with cloud technologies. The price drop of such applications along with industry 4.0 trending, triggered an explosive growth and demand for many IT modern solutions. From an industrial point of view, sensorization practices are spreading through factories and warehouses where software is constantly adapting to provide actionable insights in a data-driven configuration. The fourth industrial revolution is empowering the manufacturers with solutions for cost reduction, which tr

APA, Harvard, Vancouver, ISO und andere Zitierweisen

4

Ma, Zhiliang, Yuan Ren, Xinglei Xiang, and Ziga Turk. "Data-driven decision-making for equipment maintenance." Automation in Construction 112 (April 2020): 103103. http://dx.doi.org/10.1016/j.autcon.2020.103103.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

5

Wadzuk, Bridget, Bridget Gile, Virginia Smith, Ali Ebrahimian, Micah Strauss, and Robert Traver. "Moving Toward Dynamic and Data-Driven GSI Maintenance." Journal of Sustainable Water in the Built Environment 7, no. 4 (2021): 02521003. http://dx.doi.org/10.1061/jswbay.0000958.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

6

Lopes Gerum, Pedro Cesar, Ayca Altay, and Melike Baykal-Gürsoy. "Data-driven predictive maintenance scheduling policies for railways." Transportation Research Part C: Emerging Technologies 107 (October 2019): 137–54. http://dx.doi.org/10.1016/j.trc.2019.07.020.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

7

Coronel, Eduardo, Benjamín Barán, and Pedro Gardel. "A Survey on Data Mining for Data-Driven Industrial Assets Maintenance." Technologies 13, no. 2 (2025): 67. https://doi.org/10.3390/technologies13020067.

Der volle Inhalt der Quelle

Annotation:

This survey presents a comprehensive review of data-driven approaches for industrial asset maintenance, emphasizing the use of data mining and machine learning techniques, including deep learning, for condition-based and predictive maintenance. It examines 534 references from 1995 to 2023, along with three additional articles from 2024 on natural language processing and large language models in industrial maintenance. The study categorizes two main techniques, four specialized approaches, and 27 methodologies, resulting in over 100 variations of algorithms tailored to specific maintenance need

APA, Harvard, Vancouver, ISO und andere Zitierweisen

8

Wolfartsberger, Josef, Jan Zenisek, and Norbert Wild. "Data-Driven Maintenance: Combining Predictive Maintenance and Mixed Reality-supported Remote Assistance." Procedia Manufacturing 45 (2020): 307–12. http://dx.doi.org/10.1016/j.promfg.2020.04.022.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

9

Devarajan, Vinodkumar. "Advancing Data Center Reliability Through AI-Driven Predictive Maintenance." European Journal of Computer Science and Information Technology 13, no. 14 (2025): 102–14. https://doi.org/10.37745/ejcsit.2013/vol13n14102114.

Der volle Inhalt der Quelle

Annotation:

The evolution of data center maintenance has undergone a transformative shift from traditional reactive and scheduled maintenance to AI-driven predictive maintenance strategies. The integration of artificial intelligence and machine learning technologies enables precise failure prediction, optimizes resource allocation, and enhances operational reliability. Advanced sensor networks and sophisticated analytics pipelines process vast amounts of operational data, while machine learning models, including neural networks, support vector machines, and decision trees, provide accurate predictions of

APA, Harvard, Vancouver, ISO und andere Zitierweisen

10

Chen, Chuang, Cunsong Wang, Ningyun Lu, Bin Jiang, and Yin Xing. "A data-driven predictive maintenance strategy based on accurate failure prognostics." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 2 (2021): 387–94. http://dx.doi.org/10.17531/ein.2021.2.19.

Der volle Inhalt der Quelle

Annotation:

Maintenance is fundamental to ensure the safety, reliability and availability of engineering systems, and predictive maintenance is the leading one in maintenance technology. This paper aims to develop a novel data-driven predictive maintenance strategy that can make appropriate maintenance decisions for repairable complex engineering systems. The proposed strategy includes degradation feature selection and degradation prognostic modeling modules to achieve accurate failure prognostics. For maintenance decision-making, the perfect time for taking maintenance activities is determined by evaluat

APA, Harvard, Vancouver, ISO und andere Zitierweisen

11

Neuhold, Johannes, Matthias Landgraf, Stefan Marschnig, and Peter Veit. "Measurement Data-Driven Life-Cycle Management of Railway Track." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 11 (2020): 685–96. http://dx.doi.org/10.1177/0361198120946007.

Der volle Inhalt der Quelle

Annotation:

Track engineers face increasing cost pressure and budget restrictions in their work today. This leads to growing difficulty in legitimizing crucial maintenance and renewal measures. As a result, infrastructure managers must ensure they invest all available financial resources as sustainably and efficiently as possible. These boundary conditions require an objective tool enabling both a component-specific condition evaluation and preventive maintenance with renewal planning. The present research introduces such a tool for railway tracks based on innovative track data analyses. This tool include

APA, Harvard, Vancouver, ISO und andere Zitierweisen

12

Udo, Wisdom, and Yar Muhammad. "Data-Driven Predictive Maintenance of Wind Turbine Based on SCADA Data." IEEE Access 9 (2021): 162370–88. http://dx.doi.org/10.1109/access.2021.3132684.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

13

Attia, Hussien Gomaa. "RCM 4.0: A Novel Digital Framework for Reliability-Centered Maintenance in Smart Industrial Systems." International Journal of Emerging Science and Engineering (IJESE) 13, no. 5 (2025): 32–43. https://doi.org/10.35940/ijese.E2595.13050425.

Der volle Inhalt der Quelle

Annotation:

<strong>Abstract: </strong>Reliability-Centered Maintenance (RCM) 4.0 introduces an AI-driven digital framework that integrates Artificial Intelligence (AI), the Industrial Internet of Things (IIoT), Digital Twins, and Big Data Analytics to enhance Reliability, Availability, Maintainability, and Safety (RAMS) in Smart Industrial Systems. As industrial environments grow increasingly complex and data-driven, traditional maintenance strategies struggle to deliver the agility and precision required for intelligent asset management. This study presents RCM 4.0 as a self-optimizing, predictive maint

APA, Harvard, Vancouver, ISO und andere Zitierweisen

14

Liao, W. Z., and Y. Wang. "Dynamic Predictive Maintenance Model Based on Data-Driven Machinery Prognostics Approach." Applied Mechanics and Materials 143-144 (December 2011): 901–6. http://dx.doi.org/10.4028/www.scientific.net/amm.143-144.901.

Der volle Inhalt der Quelle

Annotation:

As an increasing number of manufacturers realize the importance of adopting new maintenance technologies to enable systems to achieve near-zero downtime, machinery prognostics which enables this paradigm shift from traditional fail-and-fix maintenance to a predict-and-prevent paradigm has arose interests from researchers. Machine's condition and degradation estimated by machinery prognostics approach can be used to support predictive maintenance policy. This paper develops a novel data-driven machine prognostics approach to assess machine's health condition and predict machine degradation. Wit

APA, Harvard, Vancouver, ISO und andere Zitierweisen

15

Valkokari, Pasi, Toni Ahonen, Helena Kortelainen, and Jesse Tervo. "The framework for data-driven maintenance planning and problem solving in maintenance communities." IFAC-PapersOnLine 55, no. 19 (2022): 175–80. http://dx.doi.org/10.1016/j.ifacol.2022.09.203.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

16

Lee, Juseong, Mihaela Mitici, Henk A. P. Blom, Pierre Bieber, and Floris Freeman. "Analyzing Emerging Challenges for Data-Driven Predictive Aircraft Maintenance Using Agent-Based Modeling and Hazard Identification." Aerospace 10, no. 2 (2023): 186. http://dx.doi.org/10.3390/aerospace10020186.

Der volle Inhalt der Quelle

Annotation:

The increasing use of on-board sensor monitoring and data-driven algorithms has stimulated the recent shift to data-driven predictive maintenance for aircraft. This paper discusses emerging challenges for data-driven predictive aircraft maintenance. We identify new hazards associated with the introduction of data-driven technologies into aircraft maintenance using a structured brainstorming conducted with a panel of maintenance experts. This brainstorming is facilitated by a prior modeling of the aircraft maintenance process as an agent-based model. As a result, we identify 20 hazards associat

APA, Harvard, Vancouver, ISO und andere Zitierweisen

17

Bhowmick, Sourav S., Byron Choi, and Curtis Dyreson. "Data-driven visual graph query interface construction and maintenance." Proceedings of the VLDB Endowment 9, no. 12 (2016): 984–92. http://dx.doi.org/10.14778/2994509.2994517.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

18

Liu, Yu, Hong-Zhong Huang, and Xiaoling Zhang. "A Data-Driven Approach to Selecting Imperfect Maintenance Models." IEEE Transactions on Reliability 61, no. 1 (2012): 101–12. http://dx.doi.org/10.1109/tr.2011.2170252.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

19

Sharma, Siddhartha, Yu Cui, Qing He, Reza Mohammadi, and Zhiguo Li. "Data-driven optimization of railway maintenance for track geometry." Transportation Research Part C: Emerging Technologies 90 (May 2018): 34–58. http://dx.doi.org/10.1016/j.trc.2018.02.019.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

20

Zhang, Zijun, Xiaofei He, and Andrew Kusiak. "Data-driven minimization of pump operating and maintenance cost." Engineering Applications of Artificial Intelligence 40 (April 2015): 37–46. http://dx.doi.org/10.1016/j.engappai.2015.01.003.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

21

Kabashkin, Igor, and Vitaly Susanin. "Unified Ecosystem for Data Sharing and AI-Driven Predictive Maintenance in Aviation." Computers 13, no. 12 (2024): 318. http://dx.doi.org/10.3390/computers13120318.

Der volle Inhalt der Quelle

Annotation:

The aviation industry faces considerable challenges in maintenance management due to the complexities of data standardization, data sharing, and predictive maintenance capabilities. This paper introduces a unified ecosystem for data sharing and AI-driven predictive maintenance designed to address these challenges by integrating real-time and historical data from diverse sources, including aircraft sensors, maintenance logs, and operational records. The proposed ecosystem enables predictive analytics and anomaly detection, enhancing decision-making processes for airlines, maintenance, repair, a

APA, Harvard, Vancouver, ISO und andere Zitierweisen

22

Ketoeva, N. L., M. A. Znamenskaya, and I. O. Borzykh. "Road map for Data-Driven approach implementation in maintenance and wearing management system of electric power equipment in management decision making." Surgut State University Journal 12, no. 4 (2024): 44–60. https://doi.org/10.35266/2949-3455-2024-4-4.

Der volle Inhalt der Quelle

Annotation:

The aim of the study is to develop a road map for implementing the Data-Driven approach in the maintenance and wearing management system of electric power equipment. The subject of the study is the Data-Driven approach in the maintenance and wearing management system of electric power equipment in management decisions making in an electric power company. The authors used the following materials and methods: dialectical, scientific knowledge and private scientific (analysis, synthesis, comparison, logical and system-structural analysis, formalization, analysis of regulatory documents), modeling

APA, Harvard, Vancouver, ISO und andere Zitierweisen

23

Bethaz, Paolo, Sara Cavaglion, Sofia Cricelli, et al. "Empowering Commercial Vehicles through Data-Driven Methodologies." Electronics 10, no. 19 (2021): 2381. http://dx.doi.org/10.3390/electronics10192381.

Der volle Inhalt der Quelle

Annotation:

In the era of “connected vehicles,” i.e., vehicles that generate long data streams during their usage through the telematics on-board device, data-driven methodologies assume a crucial role in creating valuable insights to support the decision-making process effectively. Predictive analytics allows anticipation of vehicle issues and optimized maintenance, reducing the resulting costs. In this paper, we focus on analyzing data collected from heavy trucks during their use, a relevant task for companies due to the high commercial value of the monitored vehicle. The proposed methodology, named TET

APA, Harvard, Vancouver, ISO und andere Zitierweisen

24

Venkatesh Peruthambi, Lahari Pandiri, Pallav Kumar Kaulwar, Hara Krishna Reddy Koppolu, Balaji Adusupalli, and Avinash Pamisetty. "Big Data-Driven Predictive Maintenance for Industrial IoT (IIoT) Systems." Metallurgical and Materials Engineering 31, no. 3 (2025): 21–30. https://doi.org/10.63278/1316.

Der volle Inhalt der Quelle

Annotation:

Big data-driven predictive maintenance is becoming a fundamental component of IIoT systems to enable failure predication proactively and streamline the scheduling process. This work examines the intersection of machine learning, digital twin technology, and optimization techniques in the context of increasing predictive maintenance efficiency and effectiveness. Four algorithms were evaluated via live IIoT sensor reading inputs: Random Forest, XGBoost, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM). The performance outcome indicates that XGBoost achieved the highest in f

APA, Harvard, Vancouver, ISO und andere Zitierweisen

25

Devarajan, Muralidhar, Bhagirathi Senthilkumar, Swethaa K. S, Sudarsan R, and Karthik A. S. "Data-Driven AI-ML Framework for Predictive Maintenance in Supply Chains." Journal of Business Analytics and Data Visualization 6, no. 2 (2025): 1–9. https://doi.org/10.46610/jbadv.2025.v06i02.001.

Der volle Inhalt der Quelle

Annotation:

This paper examines the role of Predictive Maintenance (PM) in enhancing supply chain resilience through the integration of Machine Learning (ML), Artificial Intelligence (AI), and the Internet of Things (IoT). PM leverages these technologies to predict equipment failures before they occur, offering a more proactive approach compared to traditional maintenance methods. By utilizing real-time data and advanced analytics, PM minimizes downtime, reduces maintenance costs, and improves overall asset reliability, resulting in optimized supply chain operations. The paper also presents a dashboard fo

APA, Harvard, Vancouver, ISO und andere Zitierweisen

26

Venkat, Kalyan Uppala. "Leveraging AI for Scalable Data Dictionaries: Enhancing Data Management and Governance in Complex Data Environments." Journal of Scientific and Engineering Research 8, no. 6 (2021): 190–96. https://doi.org/10.5281/zenodo.13758620.

Der volle Inhalt der Quelle

Annotation:

As organizations increasingly rely on data-driven strategies, the need for an adaptable and scalable data dictionary has become paramount. Traditional methods of managing data dictionaries, which involve manual documentation and maintenance, are becoming inadequate in the face of rapidly expanding and complex data environments. This paper explores how Artificial Intelligence (AI) can revolutionize the development and expansion of data dictionaries, making them more dynamic, accurate, and responsive to real-time changes. By automating metadata management, enhancing data quality, and providing p

APA, Harvard, Vancouver, ISO und andere Zitierweisen

27

Corman, Francesco, Sander Kraijema, Milinko Godjevac, and Gabriel Lodewijks. "Optimizing preventive maintenance policy: A data-driven application for a light rail braking system." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 231, no. 5 (2017): 534–45. http://dx.doi.org/10.1177/1748006x17712662.

Der volle Inhalt der Quelle

Annotation:

This article presents a case study determining the optimal preventive maintenance policy for a light rail rolling stock system in terms of reliability, availability, and maintenance costs. The maintenance policy defines one of the three predefined preventive maintenance actions at fixed time-based intervals for each of the subsystems of the braking system. Based on work, maintenance, and failure data, we model the reliability degradation of the system and its subsystems under the current maintenance policy by a Weibull distribution. We then analytically determine the relation between reliabili

APA, Harvard, Vancouver, ISO und andere Zitierweisen

28

Farahani, Saeed, Vinayak Khade, Shouvik Basu, and Srikanth Pilla. "A data-driven predictive maintenance framework for injection molding process." Journal of Manufacturing Processes 80 (August 2022): 887–97. http://dx.doi.org/10.1016/j.jmapro.2022.06.013.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

29

张, 晓东. "Application of Data-Driven Tire Life Cycle Maintenance Management System." Modern Management 14, no. 08 (2024): 1747–52. http://dx.doi.org/10.12677/mm.2024.148203.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

30

Liu, Jing, Yong Feng Dong, Yan Li, Si Yuan Lei, and Shu Qun He. "Composite Fault Diagnosis and Intelligent Maintenance Based on Data Driven." Key Engineering Materials 693 (May 2016): 1357–60. http://dx.doi.org/10.4028/www.scientific.net/kem.693.1357.

Der volle Inhalt der Quelle

Annotation:

For composite fault is difficult to diagnose, the characteristics of the large amount of data. This paper presents a method of The Prediction method of Composite Fault Based on data driven to establish intelligence unit Based on a collection of virtual individuals associated with the virtual failure associated collection and virtual behavior associated collection. Composite fault warning engine modeling is proposed, and give the warning value of composite fault finally. This method is fully assessing the future "dominant state" on the basis of the fully aware of current "hidden state". The imp

APA, Harvard, Vancouver, ISO und andere Zitierweisen

31

Tichy, Tomas, Jiri Broz, Jiri Stefan, and Rastislav Pirnik. "Failure analysis and data-driven maintenance of road tunnel equipment." Results in Engineering 18 (June 2023): 101034. http://dx.doi.org/10.1016/j.rineng.2023.101034.

Der volle Inhalt der Quelle

APA, Harvard, Vancouver, ISO und andere Zitierweisen

32

Akash Vijayrao Chaudhari and Pallavi Ashokrao Charate. "Proactive Data Pipeline Maintenance via Machine Learning-Driven Anomaly Detection." International Journal of Scientific Research in Science and Technology 12, no. 2 (2025): 1041–53. https://doi.org/10.32628/ijsrst251222663.

Der volle Inhalt der Quelle

Annotation:

Modern data pipelines are the backbone of data-driven enterprises, feeding analytics and machine learning systems with timely and accurate data. Ensuring these pipelines operate reliably is critical, as failures or data quality issues can propagate downstream and lead to significant business losses. Traditional pipeline maintenance is largely reactive—engineers respond to broken jobs or corrupted data after the fact. In this paper, we propose a proactive maintenance framework that leverages machine learning-driven anomaly detection to continuously monitor data pipelines and address issues befo

APA, Harvard, Vancouver, ISO und andere Zitierweisen

33

Wang, Hai, Su Xie, Ke Li, and M. Ahmad. "Big Data-Driven Cellular Information Detection and Coverage Identification." Sensors 19, no. 4 (2019): 937. http://dx.doi.org/10.3390/s19040937.

Der volle Inhalt der Quelle

Annotation:

As one of the core data assets of telecom operators, base station almanac (BSA) plays an important role in the operation and maintenance of mobile networks. It is also an important source of data for the location-based service (LBS) providers. However, it is always less timely updated, nor it is accurate enough. Besides, it is not open to third parties. Conventional methods detect only the location of the base station (BS) which cannot satisfy the needs of network optimization and maintenance. Because of these drawbacks, in this paper, a big-data driven method of BSA information detection and

APA, Harvard, Vancouver, ISO und andere Zitierweisen

34

Venkata Siva Prasad Maddala. "Data-Driven Manufacturing: Leveraging Analytics for Operational Excellence." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 884–93. https://doi.org/10.32628/cseit25111291.

Der volle Inhalt der Quelle

Annotation:

This comprehensive article explores the transformative impact of data-driven analytics on modern manufacturing operations, emphasizing its role in enhancing operational excellence and innovation. The article examines five key areas: production intelligence, supply chain optimization, sustainable manufacturing analytics, data-driven innovation, and implementation frameworks. Through detailed analysis, the article demonstrates how advanced analytics capabilities are revolutionizing manufacturing processes by enabling predictive maintenance, optimizing supply chains, promoting sustainable practic

APA, Harvard, Vancouver, ISO und andere Zitierweisen

35

Naveen Reddy Singi Reddy. "AI-driven data integration: Transforming enterprise data pipelines through machine learning." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 729–38. https://doi.org/10.30574/wjaets.2025.15.1.0245.

Der volle Inhalt der Quelle

Annotation:

This article examines the transformative impact of artificial intelligence on enterprise data integration processes, with a particular focus on how machine learning algorithms are revolutionizing traditional approaches to data mapping, transformation, and maintenance. The article explores the evolution from manual integration methodologies to intelligent, self-adjusting data pipelines that automatically respond to changing data patterns and requirements. The article identifies key machine learning techniques enabling automated schema matching, intelligent anomaly detection, and advanced data c

APA, Harvard, Vancouver, ISO und andere Zitierweisen

36

M, Monisha. "Predictive Maintenance of Aircraft Components Based on Sensor Data-Driven Approach: A Review." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 1338–45. http://dx.doi.org/10.22214/ijraset.2023.53843.

Der volle Inhalt der Quelle

Annotation:

Abstract: Predictive maintenance has gained significant attention in the aviation industry as a proactive strategy for enhancing aircraft safety, reducing downtime, and optimizing maintenance costs. Ensuring the reliability and efficiency of aircraft components has consistently been a significant focus in the aviation industry. Accurately anticipating possible malfunctions can significantly improve the dependability of these components and system fault detection and prediction in the aircraft industry play a critical role in preventing failures, minimizing maintenance expenses, and maximizing

APA, Harvard, Vancouver, ISO und andere Zitierweisen

37

Reddy Katta, Srikanth. "Leveraging Power BI for Data-Driven Decision-Making in Pharma Maintenance Operations." Journal of Research in Business and Management 13, no. 2 (2025): 61–70. https://doi.org/10.35629/3002-13026170.

Der volle Inhalt der Quelle

Annotation:

The pharmaceutical industry thus has many pieces of equipment that are very critical and very sensitive to breakdowns, and these are used in almost all the steps of production and are very crucial in ensuring that they meet the set regulatory requirements as well as achieve the best quality in production. Most of the maintenance undertakings are carried out following the ‘break and fix’ or ‘run and correct’ method, which results in poor performance, unanticipated machine failure, and high costs. The use of business intelligence tools such as Power BI is a revolution in the business fraternity

APA, Harvard, Vancouver, ISO und andere Zitierweisen

38

Yao, Siya, Qi Kang, Mengchu Zhou, Abdullah Abusorrah, and Yusuf Al-Turki. "Intelligent and Data-Driven Fault Detection of Photovoltaic Plants." Processes 9, no. 10 (2021): 1711. http://dx.doi.org/10.3390/pr9101711.

Der volle Inhalt der Quelle

Annotation:

Most photovoltaic (PV) plants conduct operation and maintenance (O&M) by periodical inspection and cleaning. Such O&M is costly and inefficient. It fails to detect system faults in time, thus causing heavy loss. To ensure their operations are at an ideal state, this work proposes an unsupervised method for intelligent performance evaluation and data-driven fault detection, which enables engineers to check PV panels in time and implement timely maintenance. It classifies monitoring data into three subsets: ideal period A, transition period S, and downturn period B. Based on A and B data

APA, Harvard, Vancouver, ISO und andere Zitierweisen

39

Shen, Bo, Jiyang Lin, and Yingqi Jia. "Data-driven urban rail vehicle critical parts maintenance system based on cloud-edge collaboration." Journal of Physics: Conference Series 2649, no. 1 (2023): 012057. http://dx.doi.org/10.1088/1742-6596/2649/1/012057.

Der volle Inhalt der Quelle

Annotation:

Abstract In view of the limitations of traditional maintenance methods for rail vehicles, this study proposes a data-driven maintenance system for critical parts of rail vehicles based on a cloud-side collaborative framework. Currently, there are several major issues with the maintenance of critical parts of rail vehicles, including long maintenance cycles, difficult troubleshooting, high maintenance costs, low maintenance efficiency, irregularities in data management, and a low level of informatization. To address these problems, a cloud-edge collaboration approach is adopted. The maintenance

APA, Harvard, Vancouver, ISO und andere Zitierweisen

40

Zhou, Juzhen, and Lihua Wang. "Application of a Nursing Data-Driven Model for Continuous Improvement of PICC Care Quality." Journal of Healthcare Engineering 2022 (March 19, 2022): 1–8. http://dx.doi.org/10.1155/2022/7982261.

Der volle Inhalt der Quelle

Annotation:

A PICC catheter maintenance network was established and managed to monitor the maintenance of catheters in placed patients throughout the process, providing homogeneous PICC catheter continuity of care for patients. Model-driven thinking is an idea for simulation system development. Model-driven architecture (MDA) is a design methodology that implements model-driven thinking and is widely used in simulation system development. Based on the requirements of nursing, the data-driven model is mainly divided into interface layer and functional service layer; this study adopts MDA technology which c

APA, Harvard, Vancouver, ISO und andere Zitierweisen

41

Antomarioni, Sara, Maurizio Bevilacqua, Domenico Potena, and Claudia Diamantini. "Defining a data-driven maintenance policy: an application to an oil refinery plant." International Journal of Quality & Reliability Management 36, no. 1 (2019): 77–97. http://dx.doi.org/10.1108/ijqrm-01-2018-0012.

Der volle Inhalt der Quelle

Annotation:

Purpose The purpose of this paper is developing a data-driven maintenance policy through the analysis of vast amount of data and its application to an oil refinery plant. The maintenance policy, analyzing data regarding sub-plant stoppages and components breakdowns within a defined time interval, supports the decision maker in determining whether it is better to perform predictive maintenance or corrective interventions on the basis of probability measurements. Design/methodology/approach The formalism applied to pursue this aim is association rules mining since it allows to discover the exist

APA, Harvard, Vancouver, ISO und andere Zitierweisen

42

Pampana, Ashish Kumar, JungHo Jeon, Soojin Yoon, Theodore J. Weidner, and Makarand Hastak. "Data-Driven Analysis for Facility Management in Higher Education Institution." Buildings 12, no. 12 (2022): 2094. http://dx.doi.org/10.3390/buildings12122094.

Der volle Inhalt der Quelle

Annotation:

Planned Preventive Maintenance (PPM) and Unplanned Maintenance (UPM) are the most common types of facility maintenance. This paper analyzes current trends and status of Facility Management (FM) practice at higher education institutions by proposing a systematic data-driven methodology using Natural Language Process (NLP) approaches, statistical analysis, risk-profile analysis, and outlier analysis. This study utilizes a descriptive database entitled “Facility Management Unified Classification Database (FMUCD)” to conduct the systematic data-driven analyses. The 5-year data from 2015 to 2019 wa

APA, Harvard, Vancouver, ISO und andere Zitierweisen

43

Carlos Eduardo Rodriguez. "Optimizing business aviation operations through predictive maintenance: A data-driven approach to aircraft lifecycle management." World Journal of Advanced Research and Reviews 23, no. 1 (2024): 3162–72. https://doi.org/10.30574/wjarr.2024.23.1.2146.

Der volle Inhalt der Quelle

Annotation:

Predictive analytics transforms aircraft lifecycle management by integrating predictive maintenance systems into business aviation. Predictive maintenance analyzes current data alongside machine learning algorithms with IoT sensors to anticipate equipment faults, which helps organizations reduce their expenses and increase their operation reliability. This investigation uses predictive data models to evaluate how predictive maintenance methods minimize unplanned breaks, maximize operational efficiency, and minimize total maintenance expenses. The studied outcomes demonstrate how reducing unexp

APA, Harvard, Vancouver, ISO und andere Zitierweisen

44

Sala, Roberto, Fabiana Pirola, Giuditta Pezzotta, and Sergio Cavalieri. "Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study." Applied Sciences 12, no. 15 (2022): 7395. http://dx.doi.org/10.3390/app12157395.

Der volle Inhalt der Quelle

Annotation:

Data availability is changing the way companies make decisions at various levels (e.g., strategical and operational). Researchers and practitioners are exploring how product–service system (PSS) providers can benefit from data availability and usage, especially when it comes to making decisions related to service delivery. One of the services that are expected to benefit most from data availability is maintenance. Through the analysis of the asset health status, service providers can make informed and timely decisions to prevent failures. Despite this, the offering of data-based maintenance se

APA, Harvard, Vancouver, ISO und andere Zitierweisen

45

Gomaa, Prof Dr Attia Hussien. "RCM 4.0: A Novel Digital Framework for Reliability-Centered Maintenance in Smart Industrial Systems." International Journal of Emerging Science and Engineering 13, no. 5 (2025): 32–43. https://doi.org/10.35940/ijese.e2595.13050425.

Der volle Inhalt der Quelle

Annotation:

Reliability-Centered Maintenance (RCM) 4.0 introduces an AI-driven digital framework that integrates Artificial Intelligence (AI), the Industrial Internet of Things (IIoT), Digital Twins, and Big Data Analytics to enhance Reliability, Availability, Maintainability, and Safety (RAMS) in Smart Industrial Systems. As industrial environments grow increasingly complex and data-driven, traditional maintenance strategies struggle to deliver the agility and precision required for intelligent asset management. This study presents RCM 4.0 as a self-optimizing, predictive maintenance paradigm, transformi

APA, Harvard, Vancouver, ISO und andere Zitierweisen

46

Madhukar Dharavath. "AI-Driven Predictive Maintenance in Data Infrastructure: A Multi-Modal Framework for Enhanced System Reliability." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 824–34. http://dx.doi.org/10.32628/cseit241061118.

Der volle Inhalt der Quelle

Annotation:

This article presents a comprehensive framework for implementing artificial intelligence-driven predictive maintenance in modern data infrastructure environments. While traditional maintenance approaches have relied on reactive or scheduled interventions, the proposed framework leverages multiple AI technologies, including machine learning, natural language processing, and reinforcement learning, to create a proactive maintenance ecosystem. The methodology integrates diverse data streams from infrastructure components, including sensor data, system logs, and historical maintenance records, to

APA, Harvard, Vancouver, ISO und andere Zitierweisen

47

Chen, Jinwei, Zhenchao Hu, Jinzhi Lu, Xiaochen Zheng, Huisheng Zhang, and Dimitris Kiritsis. "A Data-Knowledge Hybrid Driven Method for Gas Turbine Gas Path Diagnosis." Applied Sciences 12, no. 12 (2022): 5961. http://dx.doi.org/10.3390/app12125961.

Der volle Inhalt der Quelle

Annotation:

Gas path fault diagnosis of a gas turbine is a complex task involving field data analysis and knowledge-based reasoning. In this paper, a data-knowledge hybrid driven method for gas path fault diagnosis is proposed by integrating a physical model-based gas path analysis (GPA) method with a fault diagnosis ontology model. Firstly, a physical model-based GPA method is used to extract the fault features from the field data. Secondly, a virtual distance mapping algorithm is developed to map the GPA result to a specific fault feature criteria individual described in the ontology model. Finally, a f

APA, Harvard, Vancouver, ISO und andere Zitierweisen

48

Vishal Goyal, Kamal Sharma, Amit Jain,. "Enhancing Reliability of Advanced Driver-Assistance Systems through Predictive Maintenance and Data-Driven Insights." Journal of Electrical Systems 20, no. 4s (2024): 508–23. http://dx.doi.org/10.52783/jes.2061.

Der volle Inhalt der Quelle

Annotation:

The advancement of Advanced Driver Assistance Systems (ADAS) marks a pivotal evolution in automotive technology, aiming to enhance road safety and driving efficiency through a wide array of functionalities like blind spot detection, emergency braking, and adaptive cruise control. This research paper delves into the operational integrity, performance metrics, and maintenance strategies of ADAS components, underpinned by a comprehensive methodology involving data collection, pre-processing, feature engineering, machine learning model development, and rigorous validation processes. Systematic ins

APA, Harvard, Vancouver, ISO und andere Zitierweisen

49

Narayanan, Srinivasan. "Transforming Preventive Maintenance Operations Through Oracle Cloud Maintenance Automation." American Journal of Applied Sciences 7, no. 07 (2025): 48–66. https://doi.org/10.37547/tajas/volume07issue07-06.

Der volle Inhalt der Quelle

Annotation:

This paper examines the transformative role of Oracle Cloud Maintenance Automation in modernizing preventive maintenance practices across organizations. By automating asset maintenance workflows, minimizing manual interventions, and incorporating predictive technologies, Oracle Cloud facilitates a shift from reactive to proactive maintenance strategies. Key capabilities—including asset tracking, maintenance forecasting, and work order automation—contribute to enhanced asset reliability, operational efficiency, and cost optimization. The study highlights critical configurations and best practic

APA, Harvard, Vancouver, ISO und andere Zitierweisen

50

Dazok Donald Jambol, Oludayo Olatoye Sofoluwe, Ayemere Ukato, and Obinna Joshua Ochulor. "Transforming equipment management in oil and gas with AI-Driven predictive maintenance." Computer Science & IT Research Journal 5, no. 5 (2024): 1090–112. http://dx.doi.org/10.51594/csitrj.v5i5.1117.

Der volle Inhalt der Quelle

Annotation:

The oil and gas industry faces significant challenges in managing equipment maintenance due to the complexity and criticality of its assets. Traditional maintenance approaches are often reactive and inefficient, leading to costly downtime and safety risks. However, the emergence of artificial intelligence (AI) and predictive maintenance technologies offers a transformative solution to these challenges. This paper explores the role of AI-driven predictive maintenance in revolutionizing equipment management in the oil and gas sector. AI-driven predictive maintenance leverages machine learning al

APA, Harvard, Vancouver, ISO und andere Zitierweisen

Wir bieten Rabatte auf alle Premium-Pläne für Autoren, deren Werke in thematische Literatursammlungen aufgenommen wurden. Kontaktieren Sie uns, um einen einzigartigen Promo-Code zu erhalten!