Journal articles: 'Predictive maintenance'

1

Ratul, MD Rakibul Islam. "MMS Predictive Maintenance Big Data Analytics." International Journal of Research Publication and Reviews 4, no. 4 (April 3, 2023): 279–83. http://dx.doi.org/10.55248/gengpi.2023.4.4.34665.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Wöstmann, R., P. Strauss, and J. Prof Deuse. "Predictive Maintenance in der Produktion*/Predictive Maintenance in production." wt Werkstattstechnik online 107, no. 07-08 (2017): 524–29. http://dx.doi.org/10.37544/1436-4980-2017-07-08-48.

Full text

Abstract:

Über neue Möglichkeiten der Vernetzung und Datenverarbeitung von Anlagen und -komponenten gewinnt die prädiktive Instandhaltung stetig an Bedeutung. Während sie insbesondere in der Luftfahrt sowie bei der Versorgungs- und Energietechnik schon seit Jahren zum Einsatz kommt, sind innerhalb der Produktion derzeit noch wenige Anwendungsfälle zu finden. Der Beitrag stellt die bisher ungenutzten Potentiale dar, indem branchenübergreifende Anwendungsfälle sowie deren Übertragbarkeit auf die Produktion und Voraussetzungen für eine erfolgreiche Einführung vorgestellt werden.   Due to new possibilities of connectivity and data processing of assets and components, predictive maintenance has gained a growing importance. While it has been used in aerospace, supply or energy technology for many years, there are still few applications to be found within production. This paper outlines unused potential in presenting a classification of existing predictive maintenance applications and their transferability to production as well as prerequisites for a successful implementation.

APA, Harvard, Vancouver, ISO, and other styles

3

Mohapatra, Alma. "Generative AI for Predictive Maintenance: Predicting Equipment Failures and Optimizing Maintenance Schedules Using AI." International Journal of Scientific Research and Management (IJSRM) 12, no. 11 (November 8, 2024): 1648–72. http://dx.doi.org/10.18535/ijsrm/v12i11.ec03.

Full text

Abstract:

Predictive maintenance has emerged as a transformative approach to managing equipment health, reducing unplanned downtime, and extending asset lifespan. Leveraging advancements in generative artificial intelligence (AI), this paper explores the role of AI-driven predictive maintenance in predicting equipment failures and optimizing maintenance schedules. Traditional maintenance strategies, such as reactive and preventive approaches, often lead to inefficiencies, increased operational costs, and unexpected breakdowns. Predictive maintenance, powered by AI, offers a proactive alternative that not only anticipates failures but also enhances scheduling efficiency, maximizing equipment uptime and reducing maintenance costs. Generative AI models, including techniques such as Generative Adversarial Networks (GANs) and reinforcement learning, have shown immense promise in learning complex patterns from historical data and simulating potential equipment failure scenarios. These AI-driven models can analyze vast and diverse data sources—including sensor readings, maintenance logs, environmental conditions, and historical failures—to provide accurate, real-time insights into equipment health. This paper details the architecture and functioning of generative AI models in predictive maintenance, emphasizing their role in both anomaly detection and failure prediction. A systematic comparison of reactive, preventive, and predictive maintenance is provided, underscoring the unique benefits and challenges of predictive maintenance. We discuss the types of data essential for predictive maintenance and present sample data structures used in model training and deployment. Additionally, this paper demonstrates how generative AI models predict equipment failures by identifying anomalous behaviors before they escalate, enabling preemptive actions. A failure probability model is presented to illustrate how failure risks evolve over time, alongside tables showcasing the critical data points in predictive maintenance. The paper also explores the optimization of maintenance schedules using generative AI, where models simulate and compare different maintenance timing strategies, ultimately minimizing downtime and maximizing productivity. However, we also acknowledge the current limitations of generative AI in this domain, including data privacy concerns, computational intensity, and the challenges of model interpretability for practical implementation. Looking forward, we examine future trends such as the integration of Internet of Things (IoT) devices and the emergence of more sophisticated AI models that will likely enhance predictive maintenance applications. This paper concludes by highlighting the transformative potential of generative AI for predictive maintenance, offering insights for industries seeking to innovate their maintenance practices and achieve superior operational resilience.

APA, Harvard, Vancouver, ISO, and other styles

4

Lu, Bin, David Durocher, and Peter Stemper. "Predictive maintenance techniques." IEEE Industry Applications Magazine 15, no. 6 (November 2009): 52–60. http://dx.doi.org/10.1109/mias.2009.934444.

Full text

Abstract:

This article discusses the importance of PdM for industrial process applications and investigates a number of emerging technologies that enable this approach, including online energy-efficiency evaluation and continuous condition monitoring. The article gives an overview of existing and future technologies that can be used in these areas. Two methods for bearing fault detection and energy-efficiency estimation are discussed. The article concludes with focus on one pilot installation at Weyerhaeuser's Containerboard Packaging Plant in Manitowoc, Wisconsin, USA, monitoring three critical induction motors: a 75-hrho blower motor, a 50-hrho hydraulic pump motor, and a 200-hp compressor motor. Finally, the field experience gained in this plant is presented as two case studies.

APA, Harvard, Vancouver, ISO, and other styles

5

Entek Scientific Corporation. "Predictive maintenance system." NDT & E International 27, no. 3 (June 1994): 172. http://dx.doi.org/10.1016/0963-8695(94)90749-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Emmanuel Augustine Etukudoh. "THEORETICAL FRAMEWORKS OF ECOPFM PREDICTIVE MAINTENANCE (ECOPFM) PREDICTIVE MAINTENANCE SYSTEM." Engineering Science & Technology Journal 5, no. 3 (March 24, 2024): 913–23. http://dx.doi.org/10.51594/estj.v5i3.946.

Full text

Abstract:

The Frameworks of EcoPFM Predictive Maintenance (PM) System presents a novel approach to maintenance optimization within eco-friendly power facilities, addressing the critical need for sustainable, efficient asset management. This paper introduces an integrated framework leveraging advanced predictive analytics, machine learning algorithms, and Internet of Things (IoT) technology to enable proactive maintenance interventions based on real-time data insights. Focusing on the context of the United States it highlights the significance of implementing such a system in the realm of eco-friendly energy infrastructure. The automotive and heavy-duty truck industries in the United States grapple with the challenge of optimizing maintenance strategies to ensure vehicle reliability, safety, and environmental sustainability. Traditional maintenance approaches, primarily reactive or scheduled maintenance, fall short in addressing the complexities of modern vehicle operations. The U.S. Department of Transportation reports that heavy-duty trucks transport approximately 70% of the nation's freight by weight, underscoring the sector's critical role in the economy. However, inefficiencies in maintenance strategies contribute to significant economic and operational setbacks. According to the American Transportation Research Institute, unscheduled truck maintenance and repairs are leading operational costs for fleets, with an average expense of 16.7 cents per mile in 2020, highlighting the financial strain of current maintenance practices. In the United States, the demand for eco-friendly power solutions is rapidly increasing, driven by a growing awareness of environmental sustainability and the imperative to reduce carbon emissions. As the nation transitions towards renewable energy sources and eco-friendly power facilities, the effective management of these assets becomes paramount to ensuring reliability, performance, and longevity. The EcoPFM PM System integrates diverse data sets sourced from eco-friendly power facilities across the USA, encompassing historical operational data, sensor readings, and environmental parameters. Through predictive analytics, the system identifies patterns and trends within these data sets to forecast equipment failures and performance degradation accurately. By prioritizing maintenance tasks based on risk assessment models and condition monitoring, the system enables organizations to optimize resource allocation, minimize downtime, and extend asset lifespan. Embracing the Frameworks of EcoPFM Predictive Maintenance System holds immense promise for organizations operating eco-friendly power facilities in the United States. By harnessing data-driven insights and proactive maintenance strategies, this system offers a pathway towards enhanced operational efficiency, cost reduction, and sustainability, ultimately contributing to the advancement of eco-friendly energy infrastructure in the nation. Keywords: Predictive Maintenance, System, ECOPFM, Technology.

APA, Harvard, Vancouver, ISO, and other styles

7

Kharchenko, K. V., A. Zh Zubets, E. I. Moskvitina, L. K. Babayan, and A. M. Laffah. "Analyzing the efficiency of implementing predictive maintenance of mining equipment based on Industry 4.0 technologies." Mining Industry Journal (Gornay Promishlennost), no. 4/2024 (August 23, 2024): 130–38. http://dx.doi.org/10.30686/1609-9192-2024-4-130-138.

Full text

Abstract:

The mining industry plays a key role in the global economy, providing raw materials to various industries. However, the operational efficiency of mining equipment remains a serious issue due to high maintenance costs and downtime caused by its failures. The relevance of the study is defined by the potential of using the Industry 4.0 technologies to improve the efficiency of mining equipment maintenance. The purpose of the work is to evaluate the efficiency of implementing predictive maintenance systems based on the Industry 4.0 technologies and to develop recommendations for their development in the industry. The methodology includes an analysis of the technology adoption level in 2013–2023, collection of the KPI data to assess the impact of predictive maintenance, studying the economic efficiency of investments, the development of models for predicting failures and optimizing maintenance strategies. The results showed a significant increase in the implementation level of the Industry 4.0 technologies, improved KPIs and high economic efficiency of investments in predictive maintenance systems. The developed models demonstrated high accuracy of failure prediction and optimization of the maintenance strategies. Recommendations are formulated for the efficient implementation of predictive maintenance systems with account for the specific features of the industry. The research has theoretical significance for the development of the predictive maintenance concept and practical value for the mining enterprises. Further research may be directed towards the development of the industry standards and the integration of predictive maintenance systems with other management processes.

APA, Harvard, Vancouver, ISO, and other styles

8

Ryshkovskyi, Oleksandr, and Markiian Lukashiv. "INSTRUMENTAL PLATFORMS FOR VIBRATION ANALYSIS IN PREDICTIVE MAINTENANCE." Measuring Equipment and Metrology 85, no. 2 (2024): 21–28. http://dx.doi.org/10.23939/istcmtm2024.02.021.

Full text

Abstract:

The article explores the benefits and importance of predictive maintenance in Industry 4.0. It is a revolutionary ap- proach that analyzes data from cyber-physical systems to predict possible equipment failures before they occur and technology applied to detect early signs of a vibration problem on equipment. Thus, downtime is minimized and production continuity is ensured.

APA, Harvard, Vancouver, ISO, and other styles

9

MOHD ALI, AHMAD ALI IMRAN, MD MAHADI HASAN IMRAN, SHAHRIZAN JAMALUDIN, AHMAD FAISAL MOHAMAD AYOB, MOHAMMED ISMAIL RUSSTAM SUHRAB, SYAMIMI MOHD NORZELI, SAIFUL BAHRI HASAN BASRI, and SAIFUL BAHRI MOHAMED. "A REVIEW OF PREDICTIVE MAINTENANCE APPROACHES FOR CORROSION DETECTION AND MAINTENANCE OF MARINE STRUCTURES." JOURNAL OF SUSTAINABILITY SCIENCE AND MANAGEMENT 19, no. 4 (April 30, 2024): 180–200. http://dx.doi.org/10.46754/jssm.2024.04.014.

Full text

Abstract:

Corrosion is a natural phenomenon that deteriorates and damages the surface of metallic material. Over time, the surface of the material deteriorates due to electrochemical reactions with the surrounding environment. If corrosion is not identified early on, it can become a major financial burden for industries, costing billions of dollars. Despite swift technological developments, preventing and maintaining corrosion progression with reactive maintenance remains difficult. Due to that, predictive maintenance has been developed to predict the deterioration, degradation, and fault over the remaining useful life of the material by using real-time data, historical data, simulation, modelling, and failure probability. Predictive maintenance allows inspectors to monitor the health and predict the corrosion level of the material. However, it is hard to predict the unexpected degradation of the material from the developed prediction model without considering the harsh environment and other external factors. Hence, there is a need to investigate these problems and their effect on predictive maintenance for corrosion detection and maintenance. Therefore, this paper reviews and compares the state-of-the-art predictive maintenance solutions developed to solve corrosion issues in various applications, industries, and academic research. The challenges and opportunities for the predictive maintenance application of corrosion detection and maintenance are also presented. This review will provide new and additional knowledge that can be used to develop prediction models for corrosion detection and maintenance, which will help prevent unexpected failures.

APA, Harvard, Vancouver, ISO, and other styles

10

Segovia-Muñoz, D., X. Serrano-Guerrero, and A. Barragán-Escandon. "Predictive maintenance in LED street lighting controlled with telemanagement system to improve current fault detection procedures using software tools." Renewable Energy and Power Quality Journal 20 (September 2022): 379–86. http://dx.doi.org/10.24084/repqj20.318.

Full text

Abstract:

Predicting the lifetime of LED light sources becomes quite challenging because the time to failure is long. The LM-80 and TM-21 methods are the main used by companies to establish the product lifetime. Accurate the RUL prediction can facilitate predictive maintenance. Predictive maintenance allows estimating when a failure will occur. In this context, the maintenance can be planned in advance, eliminating unplanned outage and maximizing the useful life of the equipment. In this work, the LM-80 and TM-21 methods are used for the acquisition and extrapolation of luminous flux data, wich are entered into an algorithm developed from an exponential degradation model. With the result obtained, it is possible to establish actions that allow predictive maintenance in LED street lighting controlled by a remote management system and achieve a longer service life.

APA, Harvard, Vancouver, ISO, and other styles

11

Rodrigues, Joao, Jose Torres Farinha, and Antonio Marques Cardoso. "Predictive Maintenance Tools – A Global Survey." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 16 (January 22, 2021): 96–109. http://dx.doi.org/10.37394/23203.2021.16.7.

Full text

Abstract:

The importance given to the maintenance in the industrial world has grown over time, with new methods, new procedures and new challenges, due to the availability of new technologies. This paper focus on a global survey about predictive maintenance tools that support predictive maintenance, from the time series and decision trees until Artificial Intelligence. The approach of the several tools that can help the prediction is holistic, because new tools do not eliminate the importance of the old ones: they are complimentary and each new tool that is developed add potential for a better prediction. Additionally, it must be emphasized that some tools, that seem new are, in practice, old tools with new and powerful computational devices, assuming a new and strategic importance nowadays.

APA, Harvard, Vancouver, ISO, and other styles

12

Kang, Ziqiu, Cagatay Catal, and Bedir Tekinerdogan. "Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks." Sensors 21, no. 3 (January 30, 2021): 932. http://dx.doi.org/10.3390/s21030932.

Full text

Abstract:

Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.

APA, Harvard, Vancouver, ISO, and other styles

13

Thomas, Édouard, Éric Levrat, Benoit Iung, and Pierre Cocheteux. "Opportune maintenance and predictive maintenance decision support." IFAC Proceedings Volumes 42, no. 4 (2009): 1603–8. http://dx.doi.org/10.3182/20090603-3-ru-2001.0368.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

B, Selvalakshmi, Vijayalakshmi P, Subha N, and Balamani T. "PREDICTIVE MAINTENANCE IN INDUSTRIAL SYSTEMS USING DATA MINING WITH FUZZY LOGIC SYSTEMS." ICTACT Journal on Soft Computing 14, no. 4 (April 1, 2024): 3361–67. http://dx.doi.org/10.21917/ijsc.2024.0472.

Full text

Abstract:

In industrial systems, predictive maintenance has emerged as a crucial strategy to minimize downtime and optimize operational efficiency. This study explores the utilization of data mining techniques, specifically fuzzy logic systems, for predictive maintenance. The background section examines the importance of predictive maintenance in industrial contexts and highlights the limitations of traditional approaches. The methodology section outlines the process of employing fuzzy logic systems for predictive maintenance, including data preprocessing, feature selection, fuzzy rule generation, and model evaluation. The contribution of this research lies in providing a comprehensive framework for implementing predictive maintenance using fuzzy logic systems, offering insights into the integration of data mining techniques with industrial systems. Results demonstrate the effectiveness of the proposed methodology in accurately predicting maintenance needs and minimizing unplanned downtime. Findings suggest that fuzzy logic systems can enhance predictive maintenance capabilities by handling uncertainties and vagueness inherent in industrial data.

APA, Harvard, Vancouver, ISO, and other styles

15

Möhring, Michael, Rainer Schmidt, Barbara Keller, Kurt Sandkuhl, and Alfred Zimmermann. "Predictive Maintenance Information Systems." International Journal of Enterprise Information Systems 16, no. 2 (April 2020): 22–37. http://dx.doi.org/10.4018/ijeis.2020040102.

Full text

Abstract:

Predictive maintenance has the potential to improve the reliability of production and service provisioning. However, there is little knowledge about the proper implementation of predictive maintenance in research and practice. Therefore, we conducted a multi-case study and investigated underlying conditions and technological aspects for implementing a predictive maintenance system and where it leads to. We found that predictive maintenance initiatives are triggered by severe impacts of failures on revenue and profit. Furthermore, successful predictive maintenance initiatives require that pre-conditions are fulfilled: Data must be available and accessible. Very important is also the support by the management. We identified four factors important for the implementation of predictive maintenance. The integration of data is highly facilitated by Cloud-based mechanisms. The detection of events is enabled by advanced analytics. The execution of predictive maintenance operations is supported by data-driven process automation and visualization.

APA, Harvard, Vancouver, ISO, and other styles

16

Zhao, Yu, Kakoli Bandyopadhyay, and Cynthia Barnes. "Predictive Maintenance Information Systems." International Journal of Enterprise Information Systems 16, no. 2 (April 2020): 54–72. http://dx.doi.org/10.4018/ijeis.2020040104.

Full text

Abstract:

Enterprise resource planning (ERP) systems allow businesses to achieve high performance through distinctive capabilities and are one of the fastest growing areas within information systems. Many universities have adopted ERP in their management information systems (MIS) curriculum to increase the marketability of their students. Drawing on the IS success model and several constructive learning theories, this study develops a model that is predictive of students' continued ERP software use intention, satisfaction, and perceived learning outcomes. SAP is the ERP system used in this study. Business students at four mid-sized state universities in the United States were surveyed. The universities are members of the SAP University Alliance. There were 373 usable responses. Partial least squares structural equation modeling (PLS-SEM) was used to empirically test the model. The findings indicate that student motivation, perceived instructor support, and ERP system quality are strong predictors of student satisfaction, and learning outcomes. Student motivation and ERP system quality, but not perceived instructor support, are also significant predictors of continued use intention.

APA, Harvard, Vancouver, ISO, and other styles

17

Ton, Bram, Rob Basten, John Bolte, Jan Braaksma, Alessandro Di Bucchianico, Philippe van de Calseyde, Frank Grooteman, et al. "PrimaVera: Synergising Predictive Maintenance." Applied Sciences 10, no. 23 (November 24, 2020): 8348. http://dx.doi.org/10.3390/app10238348.

Full text

Abstract:

The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions.

APA, Harvard, Vancouver, ISO, and other styles

18

Matsuoka, Yasuo. "Predictive maintenance for ALL." Proceedings of Manufacturing Systems Division Conference 2017 (2017): 103. http://dx.doi.org/10.1299/jsmemsd.2017.103.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Lewin, Daniel R., and Yoav Harmaty. "Predictive Maintenance using PCA." IFAC Proceedings Volumes 27, no. 2 (May 1994): 439–44. http://dx.doi.org/10.1016/s1474-6670(17)48189-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Schmidt, Bernard, and Lihui Wang. "Cloud-enhanced predictive maintenance." International Journal of Advanced Manufacturing Technology 99, no. 1-4 (June 5, 2016): 5–13. http://dx.doi.org/10.1007/s00170-016-8983-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Lewin, D. R. "Predictive maintenance using PCA." Control Engineering Practice 3, no. 3 (March 1995): 415–21. http://dx.doi.org/10.1016/0967-0661(95)00015-m.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Schenck Limited. "Low cost predictive maintenance." NDT & E International 26, no. 6 (December 1993): 329. http://dx.doi.org/10.1016/0963-8695(93)90151-j.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Li, J., J. Milisavljevic-Syed, and K. Salonitis. "Predictive Maintenance Servitisation Pathways." IFAC-PapersOnLine 58, no. 8 (2024): 329–34. http://dx.doi.org/10.1016/j.ifacol.2024.08.142.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Nentwich, Corbinian, Maximilian Benker, Johannes Ellinger, Simon Zhai, Robin Kleinwort, Gunther Reinhart, and F. Michael Zäh. "Predictive Maintenance in der Produktion/Predictive Maintenance within the industrial value chain." wt Werkstattstechnik online 110, no. 03 (2020): 98–102. http://dx.doi.org/10.37544/1436-4980-2020-03-14.

Full text

Abstract:

Ungeplante Maschinenausfälle führen zu Stillständen in der Produktion, die große Auswirkungen auf die Wertschöpfungskette eines Unternehmens haben. Der Einsatz von Predictive Maintenance (PdM) entlang dieser Kette erhöht die Maschinenverfügbarkeit und sichert einen reibungslosen Produktionsablauf. Im Rahmen verschiedener Forschungsprojekte am iwb werden Anwendungsfälle für PdM in der Fertigung, der Montage und der Produktionssteuerung betrachtet. Dieser Beitrag beleuchtet individuelle Herausforderungen, Lösungsansätze und Grenzen von PdM im produktionstechnischen Umfeld.   Unplanned machine downtimes can have a big impact on the value chain of a company. Predictive Maintenance (PdM) shows the potential to increase machine availability and secures smooth production processes. Different research projects at iwb examine applications of PdM within manufacturing and assembly, as well as the integration of such approaches in production planning. This article highlights individual challenges, solutions and limits of PdM within production.

APA, Harvard, Vancouver, ISO, and other styles

25

Cai, Yiwei, John J. Hasenbein, Erhan Kutanoglu, and Melody Liao. "SINGLE-MACHINE MULTIPLE-RECIPE PREDICTIVE MAINTENANCE." Probability in the Engineering and Informational Sciences 27, no. 2 (March 28, 2013): 209–35. http://dx.doi.org/10.1017/s0269964812000423.

Full text

Abstract:

This paper studies a multiple-recipe predictive maintenance problem with M/G/1 queueing effects. The server degrades according to a discrete-time Markov chain and we assume that the controller knows both the machine status and the current number of jobs in the system. The controller's objective is to minimize total discounted costs or long-run average costs which include preventative and corrective maintenance costs, holdings costs, and possibly production costs. An optimal policy determines both when to perform maintenance and which type of job to process. Since the policy takes into account the machine's degradation status, such control decisions are known as predictive maintenance policies. In the single-recipe case, we prove that the optimal policy is monotone in the machine status, but not in the number of jobs in the system. A similar monotonicity result holds in the two-recipe case. Finally, we provide computational results indicating that significant savings can be realized when implementing a predictive maintenance policies instead of a traditional job-based threshold policy for preventive maintenances.

APA, Harvard, Vancouver, ISO, and other styles

26

Нікул, С., А. Дерев'янчук, О. Кравчук, Д. Максимчук, and Ю. Сініло. "ВПРОВАДЖЕННЯ КОНЦЕПЦІЇ «PREDICTIVE MAINTENANCE» ДЛЯ ЗБІЛЬШЕННЯ НАДІЙНОСТІ ТА ПЕРЕДБАЧУВАНОСТІ РЕМОНТНИХ РОБІТ." Collection of scientific works of Odesa Military Academy, no. 20 (December 14, 2023): 51–55. http://dx.doi.org/10.37129/2313-7509.2023.20.51-55.

Full text

Abstract:

This research article discusses the importance of implementing the Predictive Maintenance concept in the context of rocket and artillery weapons to improve the reliability and predictability of repair work. The article discusses key aspects of this concept, including its basic principles and methods, including the use of data analytics and artificial intelligence. The authors analyze the benefits of Predictive Maintenance in the military context, pointing to reduced repair costs, increased availability of military equipment, and extended equipment life. In addition, the article examines the challenges that arise when implementing this concept and provides recommendations on how to overcome them. Modern military equipment requires a high level of reliability and combat readiness. The introduction of the Predictive Maintenance concept is becoming extremely important to ensure the efficiency and safety of military equipment. This research article is devoted to the study and discussion of the possibilities of implementing Predictive Maintenance in the context of rocket and artillery weapons in order to increase the reliability and predictability of repair work. In conclusion, the article emphasizes the importance of implementing Predictive Maintenance as a strategic tool to ensure optimal performance and readiness of missile and artillery weapons in the modern military environment. Keywords: Implementation, concept, Predictive Maintenance, reliability, predictability, repair work, data analytics, artificial intelligence, monitoring systems, machine learning algorithms, sensors, failure prediction, planning, efficiency, readiness, rocket and artillery weapons.

APA, Harvard, Vancouver, ISO, and other styles

27

Lin, Chin-Yi, Yu-Ming Hsieh, Fan-Tien Cheng, Hsien-Cheng Huang, and Muhammad Adnan. "Time Series Prediction Algorithm for Intelligent Predictive Maintenance." IEEE Robotics and Automation Letters 4, no. 3 (July 2019): 2807–14. http://dx.doi.org/10.1109/lra.2019.2918684.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Dosluoglu, Taner, and Martin MacDonald. "Circuit Design for Predictive Maintenance." Advances in Artificial Intelligence and Machine Learning 02, no. 04 (2022): 533–39. http://dx.doi.org/10.54364/aaiml.2022.1136.

Full text

Abstract:

Industry 4.0 has become a driver for the entire manufacturing industry. Smart systems have enabled 30% productivity increases and predictive maintenance has been demonstrated to provide a 50% reduction in machine downtime. So far, the solution has been based on data analytics which has resulted in a proliferation of sensing technologies and infrastructure for data acquisition, transmission and processing. At the core of factory operation and automation are circuits that control and power factory equipment, innovative circuit design has the potential to address many system integration challenges. We present a new circuit design approach based on circuit level artificial intelligence solutions, integrated within control and calibration functional blocks during circuit design, improving the predictability and adaptability of each component for predictive maintenance. This approach is envisioned to encourage the development of new EDA tools such as automatic digital shadow generation and product lifecycle models, that will help identification of circuit parameters that adequately define the operating conditions for dynamic prediction and fault detection. Integration of a supplementary artificial intelligence block within the control loop is considered for capturing nonlinearities and gain/bandwidth constraints of the main controller and identifying changes in the operating conditions beyond the response of the controller. System integration topics are discussed regarding integration within OPC Unified Architecture and predictive maintenance interfaces,providing real-time updates to the digital shadow that help maintain an accurate, virtual replica model of the physical system.

APA, Harvard, Vancouver, ISO, and other styles

29

Frederiksen, Rasmus Dovnborg, Grzegorz Bocewicz, Grzegorz Radzki, Zbigniew Banaszak, and Peter Nielsen. "Cost-Effectiveness of Predictive Maintenance for Offshore Wind Farms: A Case Study." Energies 17, no. 13 (June 26, 2024): 3147. http://dx.doi.org/10.3390/en17133147.

Full text

Abstract:

The successful implementation of predictive maintenance for offshore wind farms suffers from a poor understanding of the consequential short-term impacts and a lack of research on how to evaluate the cost-efficiency of such efforts. This paper aims to develop a methodology to explore the short-term marginal impacts of predictive maintenance applied to an already existing preventive maintenance strategy. This method will be based on an analysis of the performance of the underlying predictive model and the costs considered under specific maintenance services. To support this analysis, we develop a maintenance efficiency measure able to estimate the efficiency of both the underlying prediction model used for predictive maintenance and the resulting maintenance efficiency. This distinction between the efficiency of the model and the service results will help point out insufficiencies in the predictive maintenance strategy, as well as facilitate calculations on the cost–benefits of the predictive maintenance implementation. This methodology is validated on a realistic case study of an annual service mission for an offshore wind farm and finds that the efficiency metrics described in this paper successfully support cost–benefit estimates.

APA, Harvard, Vancouver, ISO, and other styles

30

Li, Changyou, Yimin Zhang, and Minqiang Xu. "Reliability-based maintenance optimization under imperfect predictive maintenance." Chinese Journal of Mechanical Engineering 25, no. 1 (January 2012): 160–65. http://dx.doi.org/10.3901/cjme.2012.01.160.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Mutsuddi, Sayan. "Machine Learning for Predictive Maintenance in Manufacturing Industries." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 1175–81. http://dx.doi.org/10.22214/ijraset.2023.50098.

Full text

Abstract:

Abstract: For manufacturing sectors to operate at peak efficiency and safety, predictive maintenance is essential. With the examination of machine data and the detection of anomalies, machine learning techniques have become an effective method for forecasting maintenance requirements. The state-of-the-art in machine learning for preventive maintenance in manufacturing industries is thoroughly reviewed. It addresses the many machine learning methods, including anomaly detection, fault diagnosis, and time-series analysis, that are utilised for predictive maintenance. It also covers many applications and case studies from diverse industries, highlighting the benefits and restrictions of machine learning for predictive maintenance. The study also provides a comprehensive technique, including data collection, preprocessing, and machine learning model training, for applying machine learning for predictive maintenance.Traditional methods of maintenance, which can be costly and ineffective, are built on a foundation of routine inspections and maintenance programmes.Machine learning techniques have shown promising results in predicting maintenance needs by analysing machine data and identifying anomalies. This article examines the application of machine learning for predictive maintenance in the industrial sector, as well as the numerous approaches used, their advantages, and their disadvantages.Analysis and comparisons with earlier studies in the subject are done on the outcomes of the machine learning approach. The study's ramifications for manufacturing industries and their maintenance procedures are covered in the article's conclusion, along with suggestions for further research and development in the area of machine learning for predictive maintenance.

APA, Harvard, Vancouver, ISO, and other styles

32

Abdulrazzq, Raghdah Adnan, Nisreen Mustafa Sajid, and Marwan Sabah Hasan. "Artificial intelligence-driven predictive maintenance in IoT systems." South Florida Journal of Development 5, no. 12 (December 13, 2024): e4781. https://doi.org/10.46932/sfjdv5n12-030.

Full text

Abstract:

The study looks at the application of AI-driven predictive maintenance in IoT systems. Predictive device failure, efficient reduction in system downtime, reduced maintenance costs, and overall efficiency in connected devices will be enabled through machine learning and deep learning algorithms. The AI models developed within this research were able to provide a prediction accuracy of 92%, while the traditional methods of maintenance were far behind at 78%. It resulted in a 35% reduction in system downtime and a 28% decrease in maintenance costs while reducing the error rate to 8%. The above results bring out the potential of AI-based solutions for real-time predictive maintenance over complex IoT networks. It concludes by indicating some further research vectors, such as the refinement of the model and the extension of AI-driven predictive maintenance for broader applications in IoT, such as smart cities and healthcare systems.

APA, Harvard, Vancouver, ISO, and other styles

33

TARIK, Mouna, Ayoub MNIAI, and Khalid JEBARI. "HYBRID FEATURE SELECTION AND SUPPORT VECTOR MACHINE FRAMEWORK FOR PREDICTING MAINTENANCE FAILURES." Applied Computer Science 19, no. 2 (June 30, 2023): 112–24. http://dx.doi.org/10.35784/acs-2023-18.

Full text

Abstract:

The main aim of predictive maintenance is to minimize downtime, failure risks and maintenance costs in manufacturing systems. Over the past few years, machine learning methods gained ground with diverse and successful applications in the area of predictive maintenance. This study shows that performing preprocessing techniques such as oversampling and features selection for failure prediction, is promising. For instance, to handle imbalanced data, the SMOTE-Tomek method is used. For features selection, three different methods can be applied: Recursive Feature Elimination, Random Forest and Variance Threshold. The data considered in this paper for simulation is used in literature; it is applied to aircraft engine sensors measurements to predict engines failure, while the predicting algorithm used is a Support Vector Machine. The results show that classification accuracy can be significantly boosted by using the preprocessing techniques.

APA, Harvard, Vancouver, ISO, and other styles

34

Hoang, Anh Trung, Phung-Anh Nguyen, Thanh Phuc Phan, Gia Tuyen Do, Huu Dung Nguyen, I.-Jen Chiu, Chu-Lin Chou, et al. "Personalised prediction of maintenance dialysis initiation in patients with chronic kidney disease stages 3–5: a multicentre study using the machine learning approach." BMJ Health & Care Informatics 31, no. 1 (April 2024): e100893. http://dx.doi.org/10.1136/bmjhci-2023-100893.

Full text

Abstract:

BackgroundOptimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3–5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among patients with CKD stages 3–5.MethodsRetrospective electronic health record data from the Taipei Medical University clinical research database were used. Newly diagnosed patients with CKD stages 3–5 between 2008 and 2017 were identified. The observation period spanned from the diagnosis of CKD stages 3–5 until the maintenance dialysis initiation or a maximum follow-up of 3 years. Predictive models were developed using patient demographics, comorbidities, laboratory data and medications. The dataset was divided into training and testing sets to ensure robust model performance. Model evaluation metrics, including area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and F1 score, were employed.ResultsA total of 6123 and 5279 patients were included for 1 year and 3 years of the model development. The artificial neural network demonstrated better performance in predicting maintenance dialysis initiation within 1 year and 3 years, with AUC values of 0.96 and 0.92, respectively. Important features such as baseline estimated glomerular filtration rate and albuminuria significantly contributed to the predictive model.ConclusionThis study demonstrates the efficacy of an ML approach in developing a highly predictive model for estimating the timing of maintenance dialysis initiation in patients with CKD stages 3–5. These findings have important implications for personalised treatment strategies, enabling improved clinical decision-making and potentially enhancing patient outcomes.

APA, Harvard, Vancouver, ISO, and other styles

35

Yang, Jia, Yongkui Sun, Yuan Cao, and Xiaoxi Hu. "Predictive Maintenance for Switch Machine Based on Digital Twins." Information 12, no. 11 (November 22, 2021): 485. http://dx.doi.org/10.3390/info12110485.

Full text

Abstract:

As a unique device of railway networks, the normal operation of switch machines involves railway safe and efficient operation. Predictive maintenance becomes the focus of the switch machine. Aiming at the low accuracy of the prediction state and the difficulty in state visualization, the paper proposes a predictive maintenance model for switch machines based on Digital Twins (DT). It constructs a DT model for the switch machine, which contains a behavior model and a rule model. The behavior model is a high-fidelity visual model. The rule model is a high-precision prediction model, which is combined with long short-term memory (LSTM) and autoregressive Integrated Moving Average model (ARIMA). Experiment results show that the model can be more intuitive with higher prediction accuracy and better applicability. The proposed DT approach is potentially practical, providing a promising idea for switching machines in predictive maintenance.

APA, Harvard, Vancouver, ISO, and other styles

36

Razali, Muhammad Najib, Ain Farhana Jamaluddin, Rohaya Abdul Jalil, and Thi Kim Nguyen. "Big data analytics for predictive maintenance in maintenance management." Property Management 38, no. 4 (May 31, 2020): 513–29. http://dx.doi.org/10.1108/pm-12-2019-0070.

Full text

Abstract:

PurposeThis research attempts to highlight the concept of big data analytics in predictive maintenance for maintenance management of government buildings in Malaysia.Design/methodology/approachThis study uses several empirical analyses such as vector autoregression (VAR), vector error correction model (VECM), ARMA model and Granger causality to analyse predictive maintenance by using big data analytics concept.FindingsThe results indicate that there are strong correlations among these variables, which indicate reciprocal predictive maintenance of maintenance management job function. The findings also showed that there are significant needs of application of big data analytics for maintenance management in Putrajaya, Malaysia, to ensure the efficient maintenance of government buildings.Originality/valueThe conducted case study has demonstrated the empirical perspective which streamlines with the big data analytics' concept in maintenance, especially for analytics' support with appropriate empirical methodology

APA, Harvard, Vancouver, ISO, and other styles

37

Mogal, Shyam, R. V. Bhandare, V. M. Phalle, and P. B. Kushare. "Fault Diagnosis and Prediction of Remaining Useful Life (RUL) of Rolling Element Bearing : A review state of art." Tribologia - Finnish Journal of Tribology 41, no. 1−2 (July 15, 2024): 28–42. http://dx.doi.org/10.30678/fjt.141503.

Full text

Abstract:

Fault diagnosis of rolling element bearings is a critical aspect of machine maintenance and reliability. Bearings are extensively used in various industrial applications, and their failure can lead to costly downtime and equipment damage. Rotating machinery under continuous overload conditions can indeed significantly degrade bearing life and lead to various other issues. To identify issues in rolling element bearings (REB), several techniques and methods are employed. Diagnosing faults in ball bearings while simultaneously estimating the Remaining Useful Life (RUL) of the bearing is a crucial aspect of predictive maintenance. This can be achieved through a combination of signal processing techniques, machine learning methods, and RUL prediction models. The estimation of a bearing Remaining Useful Life (RUL) is of significant importance in predictive maintenance strategies to avoid unexpected failures, reduce downtime, and optimize maintenance costs. This literature review aims to explore the methodologies, techniques, and advancements in predicting the remaining useful life of bearings.

APA, Harvard, Vancouver, ISO, and other styles

38

Hadjidemetriou, Georgios M., Xiang Xie, and Ajith K. Parlikad. "Predictive Group Maintenance Model for Networks of Bridges." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 4 (March 10, 2020): 373–83. http://dx.doi.org/10.1177/0361198120912226.

Full text

Abstract:

Recent progress in the monitoring and prediction of the condition of infrastructure using sensing technologies has motivated researchers and infrastructure owners to explore the benefits of asset predictive maintenance, as an alternative to reactive maintenance. However, the application of predictive group maintenance for multi-system multi-component networks (MSMCN) has not received much attention in the literature or in practice. The paper presents an approach that prioritizes the maintenance of MSMCN of bridges, using a deterioration model of components with uncertainty, a lifecycle cost model, a predictive model for the optimal time for maintenance based on the latest inspection, a group maintenance model to reduce setup cost, and a scheduling model considering budget constraints. This model has been applied to a network of 15 bridges constituted by multiple heterogeneous components, and, compared with the Structures Investment Toolkit, it showed potential for a substantial decrease in maintenance costs, thus highlighting the practical significance of the presented approach.

APA, Harvard, Vancouver, ISO, and other styles

39

Sresakoolchai, Jessada, and Sakdirat Kaewunruen. "Track Geometry Prediction Using Three-Dimensional Recurrent Neural Network-Based Models Cross-Functionally Co-Simulated with BIM." Sensors 23, no. 1 (December 30, 2022): 391. http://dx.doi.org/10.3390/s23010391.

Full text

Abstract:

Railway track maintenance plays an important role in enabling safe, reliable, and seamless train operations and passenger comfort. Due to the increasing rail transportation, rolling stocks tend to run faster and the load tends to increase continuously. As a result, the track deteriorates quicker, and maintenance needs to be performed more frequently. However, more frequent maintenance activities do not guarantee a better overall performance of the railway system. It is crucial for rail infrastructure managers to optimize predictive and preventative maintenance. This study is the world’s first to develop deep machine learning models using three-dimensional recurrent neural network-based co-simulation models to predict track geometry parameters in the next year. Different recurrent neural network-based techniques are used to develop predictive models. In addition, a building information modeling (BIM) model is developed to integrate and cross-functionally co-simulate the track geometry measurement with the prediction for predictive and preventative maintenance purposes. From the study, the developed BIM models can be used to exchange information for predictive maintenance. Machine learning models provide the average R2 of 0.95 and the average mean absolute error of 0.56 mm. The insightful breakthrough demonstrates the potential of machine learning and BIM for predictive maintenance, which can promote the safety and cost effectiveness of railway maintenance.

APA, Harvard, Vancouver, ISO, and other styles

40

Tong, Guoqiang, Xinbo Qian, and Yilai Liu. "Prognostics and Predictive Maintenance Optimization Based on Combination BP-RBF-GRNN Neural Network Model and Proportional Hazard Model." Journal of Sensors 2022 (April 29, 2022): 1–17. http://dx.doi.org/10.1155/2022/8655669.

Full text

Abstract:

Owning to the advantage of keeping the operating environment safe, high reliability, and low production cost, predictive maintenance has been widely used in industry and academia. Predictive maintenance based on degeneration state mainly studies the degeneration prediction. However, on account of the error of the sensor and human, condition monitoring data may not directly reflect the true degeneration. The degeneration model with dynamic explanatory covariates which is named as proportional hazard model is proposed to deal with the semi-observed monitoring condition. And the degeneration prediction mainly adopts a single prediction model, which leads to low prediction accuracy. A combination forecasting model can effectively solve the above problem. Compared to the traditional prediction method, the neural network model can use the “black box” characteristic to indirectly construct the degeneration model without complex mathematical derivation. Therefore, we propose a combination BP-RBF-GRNN neural network model which is applied to improve the degeneration prediction with dynamic covariate. Based on the above two aspects, a predictive maintenance optimization framework based on the proportional hazard model and BP-RBF-GRNN neural network model is proposed to improve maintenance efficiency and reduce maintenance costs. The simulation results of thrust ball bearing show that the proposed method can effectively improve the degeneration prediction accuracy and reduce the maintenance cost rate to a certain extent.

APA, Harvard, Vancouver, ISO, and other styles

41

Okimoto, Tasuku. "Predictive Maintenance System for Extruders." Seikei-Kakou 33, no. 2 (January 20, 2021): 54–56. http://dx.doi.org/10.4325/seikeikakou.33.54.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Watanabe, Takeshi, and Masahiko Ooishi. "Predictive Maintenance Systems for Substation." IEEJ Transactions on Power and Energy 112, no. 6 (1992): 455–60. http://dx.doi.org/10.1541/ieejpes1990.112.6_455.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Patel, Prakash, Pallavi Jain, Swapnil Bhambure, Yashraj Sen, and N. F. Shaikh. "Predictive Maintenance Approach on Automobiles." International Journal of Computer Sciences and Engineering 6, no. 12 (December 31, 2018): 763–67. http://dx.doi.org/10.26438/ijcse/v6i12.763767.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Kamel, H. "Artificial intelligence for predictive maintenance." Journal of Physics: Conference Series 2299, no. 1 (July 1, 2022): 012001. http://dx.doi.org/10.1088/1742-6596/2299/1/012001.

Full text

Abstract:

Abstract Maintenance constitutes an important share of modern industrial activities. Reliable operations rely on the adequate application on maintenance. However, in the present competitive environment, maintenance processes must be optimized so that they will be performed only when needed, otherwise resources will be needlessly wasted. This is in contrast to the conventional approach where maintenance is scheduled according to a time plan regardless of it is needed or not. This paper presents the application of artificial intelligence to create a model that can successfully predict the condition of a machine in terms of the probability of failure occurrence. This work uses a synthetic dataset that reflects a realistic scenario where sensors are connected to a machine to monitor its health condition and record failure incidents. The dataset consists of 10,000 records. Each record consists of five numerical measurements: air and process temperatures, machine rotational speed and torque and finally a measurement of machine wear. This in addition to the type of product the machine is producing makes six input variables. The output response was considered as the state of machine failure that was represented as true or false. An artificial neural network was trained and it successfully predicted the state of the machine. The capabilities and limitations of applying artificial intelligence were discussed. In addition, a brief overview of other predictive techniques was given.

APA, Harvard, Vancouver, ISO, and other styles

45

Segerstrom, Martin. "Predictive Maintenance of Paint Shops." IST International Surface Technology 9, no. 3 (November 2016): 6–7. http://dx.doi.org/10.1007/s35724-016-0045-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Olivan, Patrick, Ulrich Hutschek, and Silvia Rummel. "Predictive Maintenance – Voraussetzungen und Potenziale." Zeitschrift für wirtschaftlichen Fabrikbetrieb 116, no. 10 (October 1, 2021): 667–72. http://dx.doi.org/10.1515/zwf-2021-0123.

Full text

Abstract:

Abstract Bei Predictive Maintenance handelt es sich im Kern um die Kommerzialisierung von Condition Monitoring. Für Entscheider stellen sich dabei folgende Fragen: Welche Geschäftsmodelle lassen sich mit Predicitve Maintenance abbilden? Und mit welchen technologischen Prinzipien kann Condition Monitoring implementiert werden? Zur Beantwortung dieser Fragen wurden relevante Technologien und Geschäftsmodelle ausgewertet und für mittelständische Unternehmen in Form von pragmatischen Richtlinien aufbereitet.

APA, Harvard, Vancouver, ISO, and other styles

47

öNEL, İzzet Y., Engin çAĞLAR, and Ahmet DUYAR. "New Horizons on Predictive Maintenance." IFAC Proceedings Volumes 42, no. 19 (2009): 120–25. http://dx.doi.org/10.3182/20090921-3-tr-3005.00023.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Wang, Hongxia, Xiaohui Ye, and Ming Yin. "Study on Predictive Maintenance Strategy." International Journal of u- and e- Service, Science and Technology 9, no. 4 (April 30, 2016): 295–300. http://dx.doi.org/10.14257/ijunesst.2016.9.4.29.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Yuan, Yong, Xiaomo Jiang, and Xian Liu. "Predictive maintenance of shield tunnels." Tunnelling and Underground Space Technology 38 (September 2013): 69–86. http://dx.doi.org/10.1016/j.tust.2013.05.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Chelbi, Anis, and Daoud Ait-Kadi. "Inspection and predictive maintenance strategies." International Journal of Computer Integrated Manufacturing 11, no. 3 (January 1998): 226–31. http://dx.doi.org/10.1080/095119298130750.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Predictive maintenance'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles