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Journal articles on the topic 'Smart predictive maintenance'

Author: Grafiati

Published: 10 March 2023

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1

Lmouatassime, Abdessalam, and Mohammed Bousmah. "Machine Learning for Predictive Maintenance with Smart Maintenance Simulator." International Journal of Computer Applications 183, no. 22 (2021): 35–40. http://dx.doi.org/10.5120/ijca2021921590.

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Nazara, Krisman Yusuf. "Perancangan Smart Predictive Maintenance untuk Mesin Produksi." Seminar Nasional Official Statistics 2022, no. 1 (2022): 691–702. http://dx.doi.org/10.34123/semnasoffstat.v2022i1.1575.

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Laju pertumbuhan ekonomi Indonesia mendapatkan kontribusi yang besar dari industri manufaktur. Di era industri 4.0, optimasi penggunaan teknologi informasi mendukung efektivitas kinerja karyawan suatu industri agar lebih produktif. Untuk mengoptimasi biaya pemeliharaan dan memonitor peralatan serta mesin produksi dibutuhkan teknologi Internet of Things (IoT) yang dilengkapi dengan machine learning untuk menghasilkan smart predictive maintenance. Penelitian ini bertujuan untuk mendapatkan model prediksi terbaik untuk klasifikasi kondisi mesin produksi dengan membandingkan berbagai model machine

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Tichý, Tomáš, Jiří Brož, Zuzana Bělinová, and Rastislav Pirník. "Analysis of Predictive Maintenance for Tunnel Systems." Sustainability 13, no. 7 (2021): 3977. http://dx.doi.org/10.3390/su13073977.

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Smart and automated maintenance could make the system and its parts more sustainable by extending their lifecycle, failure detection, smart control of the equipment, and precise detection and reaction to unexpected circumstances. This article focuses on the analysis of data, particularly on logs captured in several Czech tunnel systems. The objective of the analysis is to find useful information in the logs for predicting upcoming situations, and furthermore, to check the possibilities of predictive diagnostics and to design the process of predictive maintenance. The main goal of the article i

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Pech, Martin, Jaroslav Vrchota, and Jiří Bednář. "Predictive Maintenance and Intelligent Sensors in Smart Factory: Review." Sensors 21, no. 4 (2021): 1470. http://dx.doi.org/10.3390/s21041470.

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With the arrival of new technologies in modern smart factories, automated predictive maintenance is also related to production robotisation. Intelligent sensors make it possible to obtain an ever-increasing amount of data, which must be analysed efficiently and effectively to support increasingly complex systems’ decision-making and management. The paper aims to review the current literature concerning predictive maintenance and intelligent sensors in smart factories. We focused on contemporary trends to provide an overview of future research challenges and classification. The paper used burst

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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.

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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 challenge

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Mahmoud, Moamin A., Naziffa Raha Md Nasir, Mathuri Gurunathan, Preveena Raj, and Salama A. Mostafa. "The Current State of the Art in Research on Predictive Maintenance in Smart Grid Distribution Network: Fault’s Types, Causes, and Prediction Methods—A Systematic Review." Energies 14, no. 16 (2021): 5078. http://dx.doi.org/10.3390/en14165078.

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With the exponential growth of science, Internet of Things (IoT) innovation, and expanding significance in renewable energy, Smart Grid has become an advanced innovative thought universally as a solution for the power demand increase around the world. The smart grid is the most practical trend of effective transmission of present-day power assets. The paper aims to survey the present literature concerning predictive maintenance and different types of faults that could be detected within the smart grid. Four databases (Scopus, ScienceDirect, IEEE Xplore, and Web of Science) were searched betwee

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Raco, F., M. Balzani, F. Planu, and A. Cittadino. "INSPIRE PROJECT: INTEGRATED TECHNOLOGIES FOR SMART BUILDINGS AND PREDICTIVE MAINTENANCE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W3-2022 (December 2, 2022): 127–33. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w3-2022-127-2022.

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Abstract. Applying integrated digital technologies for the management and maintenance of the existing built heritage appears to be one of the main current challenges for the definition and application of digitisation protocols for the construction supply chain. Key enabling technologies, collaborative platforms, Big Data management and information integration in a BIM environment are areas of increasing experimentation. In the field of intervention on the built heritage, it is the boundaries and opportunities offered by the integration of many different information sources that constitutes the

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Çınar, Zeki Murat, Abubakar Abdussalam Nuhu, Qasim Zeeshan, Orhan Korhan, Mohammed Asmael, and Babak Safaei. "Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0." Sustainability 12, no. 19 (2020): 8211. http://dx.doi.org/10.3390/su12198211.

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Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim t

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Abdallah, Mustafa, Byung-Gun Joung, Wo Jae Lee, et al. "Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets." Sensors 23, no. 1 (2023): 486. http://dx.doi.org/10.3390/s23010486.

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Smart manufacturing systems are considered the next generation of manufacturing applications. One important goal of the smart manufacturing system is to rapidly detect and anticipate failures to reduce maintenance cost and minimize machine downtime. This often boils down to detecting anomalies within the sensor data acquired from the system which has different characteristics with respect to the operating point of the environment or machines, such as, the RPM of the motor. In this paper, we analyze four datasets from sensors deployed in manufacturing testbeds. We detect the level of defect for

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Chen, Lei, Lijun Wei, Yu Wang, Junshuo Wang, and Wenlong Li. "Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors." Sensors 22, no. 6 (2022): 2106. http://dx.doi.org/10.3390/s22062106.

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Centrifugal pumps have a wide range of applications in industrial and municipal water affairs. During the use of centrifugal pumps, failures such as bearing wear, blade damage, impeller imbalance, shaft misalignment, cavitation, water hammer, etc., often occur. It is of great importance to use smart sensors and digital Internet of Things (IoT) systems to monitor the real-time operating status of pumps and predict potential failures for achieving predictive maintenance of pumps and improving the intelligence level of machine health management. Firstly, the common fault forms of centrifugal pump

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Georgievskaia, Evgeniia. "Predictive analytics as a way to smart maintenance of hydraulic turbines." Procedia Structural Integrity 28 (2020): 836–42. http://dx.doi.org/10.1016/j.prostr.2020.10.098.

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Hogland, William, Christos Katrantsiotis, and Muhammad Asim Ibrahim. "Baltic Smart Asset Management – data driven predictive maintenance methods for future." IOP Conference Series: Earth and Environmental Science 578 (November 4, 2020): 012035. http://dx.doi.org/10.1088/1755-1315/578/1/012035.

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Sundaram, Karthik Trichur. "Digital Transformation with AI/ML & Cybersecurity." International Journal of Computer Science and Mobile Computing 11, no. 11 (2022): 1–3. http://dx.doi.org/10.47760/ijcsmc.2022.v11i11.001.

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Artificial Intelligence (AI) and Machine Learning (ML) have impacted the manufacturing industry, especially in the industry 4.0 paradigm. It encourages the usage of smart devices, sensors, and machines for production. Moreover, AI techniques and ML algorithms give predictive insights into various manufacturing tasks, such as predictive maintenance, continuous inspection, process optimization, quality improvement, and more. However, there are many open concerns and challenges regarding cybersecurity in smart manufacturing.

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Jovančić, Predrag, Dragan Ignjatović, Stevan Đenadić, Miloš Tanasijević, and Filip Miletić. "Koncept prediktivnog održavanja 4.0 (PdM) u energetici – konekcija sa budućom primenom Industrije 5.0." Energija, ekonomija, ekologija XXIV, no. 2 (2022): 54–60. http://dx.doi.org/10.46793/eee22-2.54j.

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Industry 4.0 marks the fourth industrial revolution, characterized by the use of cyber-physical systems. In order to achieve an optimal maintenance strategy (but also production), it is necessary to develop systems that support advanced intelligent maintenance systems or smart maintenance technologies. This resulted in the postulates of Predictive Maintenance 4.0, which define the very near future in the field of maintenance of technical systems. Predictive Maintenance 4.0 involves harnessing the power of artificial intelligence to create ongoing insights into detecting causes and anomalies in

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May, Gokan, Sangje Cho, AmirHossein Majidirad, and Dimitris Kiritsis. "A Semantic Model in the Context of Maintenance: A Predictive Maintenance Case Study." Applied Sciences 12, no. 12 (2022): 6065. http://dx.doi.org/10.3390/app12126065.

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Advanced technologies in modern industry collect massive volumes of data from a plethora of sources, such as processes, machines, components, and documents. This also applies to predictive maintenance. To provide access to these data in a standard and structured way, researchers and practitioners need to design and develop a semantic model of maintenance entities to build a reference ontology for maintenance. To date, there have been numerous studies combining the domain of predictive maintenance and ontology engineering. However, such earlier works, which focused on semantic interoperability

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Klees, Marina, and Safa Evirgen. "Building a smart database for predictive maintenance in already implemented manufacturing systems." Procedia Computer Science 204 (2022): 14–21. http://dx.doi.org/10.1016/j.procs.2022.08.002.

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Lao, Liangfeng, Matthew Ellis, and Panagiotis D. Christofides. "Smart manufacturing: Handling preventive actuator maintenance and economics using model predictive control." AIChE Journal 60, no. 6 (2014): 2179–96. http://dx.doi.org/10.1002/aic.14427.

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Vicente-Gabriel, Jorge, Ana-Belén Gil-González, Ana Luis-Reboredo, Pablo Chamoso, and Juan M. Corchado. "LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities." Electronics 10, no. 1 (2021): 78. http://dx.doi.org/10.3390/electronics10010078.

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Predictive maintenance is a field of research that has emerged from the need to improve the systems in place. This research focuses on controlling the degradation of photovoltaic (PV) modules in outdoor solar panels, which are exposed to a variety of climatic loads. Improved reliability, operation, and performance can be achieved through monitoring. In this study, a system capable of predicting the output power of a solar module was implemented. It monitors different parameters and uses automatic learning techniques for prediction. Its use improved reliability, operation, and performance. On t

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Pan, Kun, and Yuchen Jiang. "Computer Prediction Model for Equipment Maintenance Using Cloud Computing and Secure Data-sharing." Journal of Physics: Conference Series 2083, no. 4 (2021): 042042. http://dx.doi.org/10.1088/1742-6596/2083/4/042042.

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Abstract A With the popularization of automation in the industrial field, productivity has been greatly improved. However, manufacturing corporations are facing a data tsunami which brings new challenges to predictive maintenance (PdM). In recent years, many approaches and architecture for predictive maintenance have been proposed to solve some of these problems to varying degrees. This paper introduces a general framework based on the Internet of Things, cloud computing and big data analytics for PdM of industrial equipment. In this framework, smart sensors are installed on the device to obta

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Raja, Hadi Ashraf, Karolina Kudelina, Bilal Asad, et al. "Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines." Energies 15, no. 24 (2022): 9507. http://dx.doi.org/10.3390/en15249507.

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Industrial revolution 4.0 has enabled the advent of new technological advancements, including the introduction of information technology with physical devices. The implementation of information technology in industrial applications has helped streamline industrial processes and make them more cost-efficient. This combination of information technology and physical devices gave birth to smart devices, which opened up a new research area known as the Internet of Things (IoT). This has enabled researchers to help reduce downtime and maintenance costs by applying condition monitoring on electrical

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Achouch, Mounia, Mariya Dimitrova, Khaled Ziane, et al. "On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges." Applied Sciences 12, no. 16 (2022): 8081. http://dx.doi.org/10.3390/app12168081.

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In the era of the fourth industrial revolution, several concepts have arisen in parallel with this new revolution, such as predictive maintenance, which today plays a key role in sustainable manufacturing and production systems by introducing a digital version of machine maintenance. The data extracted from production processes have increased exponentially due to the proliferation of sensing technologies. Even if Maintenance 4.0 faces organizational, financial, or even data source and machine repair challenges, it remains a strong point for the companies that use it. Indeed, it allows for mini

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Vlasov, Andrey I., Pavel V. Grigoriev, Aleksey I. Krivoshein, Vadim A. Shakhnov, Sergey S. Filin, and Vladimir S. Migalin. "Smart management of technologies: predictive maintenance of industrial equipment using wireless sensor networks." Entrepreneurship and Sustainability Issues 6, no. 2 (2018): 489–502. http://dx.doi.org/10.9770/jesi.2018.6.2(2).

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Massaro, Alessandro, Sergio Selicato, and Angelo Galiano. "Predictive Maintenance of Bus Fleet by Intelligent Smart Electronic Board Implementing Artificial Intelligence." IoT 1, no. 2 (2020): 180–97. http://dx.doi.org/10.3390/iot1020012.

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This paper is focused on the design and development of a smart and compact electronic control unit (ECU) for the monitoring of a bus fleet. The ECU system is able to extract all vehicle data by the on-board diagnostics-(ODB)-II and SAE J1939 standards. The integrated system Internet of Things (IoT) system, is interconnected in the cloud by an artificial intelligence engine implementing multilayer perceptron artificial neural network (MLP-ANN) and is able to predict maintenance of each vehicle by classifying the driver behavior. The key performance indicator (KPI) of the driver behavior has bee

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Coupry, Corentin, Sylvain Noblecourt, Paul Richard, David Baudry, and David Bigaud. "BIM-Based Digital Twin and XR Devices to Improve Maintenance Procedures in Smart Buildings: A Literature Review." Applied Sciences 11, no. 15 (2021): 6810. http://dx.doi.org/10.3390/app11156810.

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In recent years, the use of digital twins (DT) to improve maintenance procedures has increased in various industrial sectors (e.g., manufacturing, energy industry, aerospace) but is more limited in the construction industry. However, the operation and maintenance (O&M) phase of a building’s life cycle is the most expensive. Smart buildings already use BIM (Building Information Modeling) for facility management, but they lack the predictive capabilities of DT. On the other hand, the use of extended reality (XR) technologies to improve maintenance operations has been a major topic of academi

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Hung, Yu-Hsin. "Improved Ensemble-Learning Algorithm for Predictive Maintenance in the Manufacturing Process." Applied Sciences 11, no. 15 (2021): 6832. http://dx.doi.org/10.3390/app11156832.

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Industrial Internet of Things (IIoT) technologies comprise sensors, devices, networks, and applications from the edge to the cloud. Recent advances in data communication and application using IIoT have streamlined predictive maintenance (PdM) for equipment maintenance and quality management in manufacturing processes. PdM is useful in fields such as device, facility, and total quality management. PdM based on cloud or edge computing has revolutionized smart manufacturing processes. To address quality management problems, herein, we develop a new calculation method that improves ensemble-learni

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Moens, Pieter, Vincent Bracke, Colin Soete, et al. "Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications." Sensors 20, no. 15 (2020): 4308. http://dx.doi.org/10.3390/s20154308.

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The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of predictive maintenance requires well-trained machine learning algorithms which on their turn require high volumes of reliable data. This paper addresses both challenges and presents the Smart Maintenance Living Lab, an open test and research platform that consists of a fleet

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Guerroum, Mariya, Mourad Zegrari, Malek Masmoudi, Mouna Berquedich, and Abdelhafid Ait Elmahjoub. "Machine Learning Technics for Remaining useful Life Prediction using Diagnosis Data: a Case Study of a Jaw Crusher." International Journal of Emerging Technology and Advanced Engineering 12, no. 10 (2022): 122–35. http://dx.doi.org/10.46338/ijetae1022_14.

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Predictive maintenance currently involves digital transformation with all the technologies developed to serve the latter. This maintenance strategy is believed to be an efficient solution to end late/early intervention issues. It is for this reason that machine health state monitoring by Remaining Useful Life prognosis is very crucial. However, in the literature, most studies focus on failure diagnosis more than the system's Remaining Useful Life. In addition, to prepare models to serve the prognosis, the use of actual machinery data is critical to assure the later scalability of the applicati

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Rákay, Róbert, and Alena Galajdová. "TESTING PROPERTIES OF SMART CONDITON MONITORING SYSTEM." TECHNICAL SCIENCES AND TECHNOLOGIES, no. 3(21) (2020): 266–73. http://dx.doi.org/10.25140/2411-5363-2020-3(21)-266-273.

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Urgency of the research. Modern trends in the automation focus on the implementation of new technologies to reduce production and maintenance costs. Maintenance and service of every industrial automation system is crucial. Target setting. When engineers try to optimize the cost of production and processes, they usually reduce maintenance cost. The latest smart monitoring systems provide significant benefits in terms of risk management and equipment failure reduction. Actual scientific researches and issues analysis. To prepare this paper, various publicly available datasheets and experimental

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Kim, Sung-An. "A Study on the Predictive Maintenance Algorithms Considering Load Characteristics of PMSMs to Drive EGR Blowers for Smart Ships." Energies 14, no. 18 (2021): 5744. http://dx.doi.org/10.3390/en14185744.

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Exhaust gas recirculation (EGR) is a NOx reduction technology that can meet stringent environmental regulatory requirements. EGR blower systems must be used to increase the exhaust gas pressure at a lower rate than the scavenging air pressure. Electric motor drive systems are essential to rotate the EGR blowers. For the effective management of the EGR blower systems in smart ships, there is a growing need for predictive maintenance technology fused with information and communication technology (ICT). Since an electric motor accounts for about 80% of electric loads driven by the EGR, it is esse

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Lee, Hyunsoo, Seok-Youn Han, and Kee-Jun Park. "Generative Adversarial Network-based Missing Data Handling and Remaining Useful Life Estimation for Smart Train Control and Monitoring Systems." Journal of Advanced Transportation 2020 (November 27, 2020): 1–15. http://dx.doi.org/10.1155/2020/8861942.

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As railway is considered one of the most significant transports, sudden malfunction of train components or delayed maintenance may considerably disrupt societal activities. To prevent this issue, various railway maintenance frameworks, from “periodic time-based and distance-based traditional maintenance frameworks” to “monitoring/conditional-based maintenance systems,” have been proposed and developed. However, these maintenance frameworks depend on the current status and situations of trains and cars. To overcome these issues, several predictive frameworks have been proposed. This study propo

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Hoffmann, Martin W., Stephan Wildermuth, Ralf Gitzel, et al. "Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions." Sensors 20, no. 7 (2020): 2099. http://dx.doi.org/10.3390/s20072099.

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The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of

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Abood, Azhar M., Ahmed R. Nasser, and Huthaifa Al-Khazraji. "Predictive Maintenance of Electromechanical Systems Using Deep Learning Algorithms: Review." Ingénierie des systèmes d information 27, no. 6 (2022): 1009–17. http://dx.doi.org/10.18280/isi.270618.

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Predictive Maintenance (PM) is a major part of smart manufacturing in the fourth industrial revolution. The classical fault diagnosis approach for complex systems such as an electromechanical system is not effective. Taking the advantage of the successful implementations of PM together with Deep Learning (DL) methods replaces the conventional diagnosis methods with modern diagnosis methods. This study intends to aid experts, engineers, and technicians in different electromechanical systems in comprehending how the PM used DL methods to find the multi-fault diagnosis. In this direction, this pa

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Calabrese, Matteo, Martin Cimmino, Francesca Fiume, et al. "SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0." Information 11, no. 4 (2020): 202. http://dx.doi.org/10.3390/info11040202.

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Predictive Maintenance (PdM) is a prominent strategy comprising all the operational techniques and actions required to ensure machine availability and to prevent a machine-down failure. One of the main challenges of PdM is to design and develop an embedded smart system to monitor and predict the health status of the machine. In this work, we use a data-driven approach based on machine learning applied to woodworking industrial machines for a major woodworking Italian corporation. Predicted failures probabilities are calculated through tree-based classification models (Gradient Boosting, Random

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Casini, Marco. "Extended Reality for Smart Building Operation and Maintenance: A Review." Energies 15, no. 10 (2022): 3785. http://dx.doi.org/10.3390/en15103785.

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The operation and maintenance (O&M) of buildings and infrastructure represent a strategic activity to ensure they perform as expected over time and to reduce energy consumption and maintenance costs at the urban and building scale. With the increasing diffusion of BIM, IoT devices, and AI, the future of O&M is represented by digital twin technology. To effectively take advantage of this digital revolution, thus enabling data-driven energy control, proactive maintenance, and predictive daily operations, it is vital that smart building management exploits the opportunities offered by the

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Mohamad Nor, Ahmad Azhari, Murizah Kassim, Mohd Sabri Minhat, and Norsuzila Ya'acob. "A review on predictive maintenance technique for nuclear reactor cooling system using machine learning and augmented reality." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (2022): 6602. http://dx.doi.org/10.11591/ijece.v12i6.pp6602-6613.

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<span lang="EN-US">Reactor TRIGA PUSPATI (RTP) is the only research nuclear reactor in Malaysia. Maintenance of RTP is crucial which affects its safety and reliability. Currently, RTP maintenance strategies used corrective and preventative which involved many sensors and equipment conditions. The existing preventive maintenance method takes a longer time to complete the entire system’s maintenance inspection. This study has investigated new predictive maintenance techniques for developing RTP predictive maintenance for primary cooling systems using machine learning (ML) and augmented rea

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Chen, Hong-Ming, Jia-Hao Zhang, Yu-Chieh Wang, Hsiang-Ching Chang, Jen-Kai King, and Chao-Tung Yang. "Hot-Pressing Furnace Current Monitoring and Predictive Maintenance System in Aerospace Applications." Sensors 23, no. 4 (2023): 2230. http://dx.doi.org/10.3390/s23042230.

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This research combines the application of artificial intelligence in the production equipment fault monitoring of aerospace components. It detects three-phase current abnormalities in large hot-pressing furnaces through smart meters and provides early preventive maintenance. Different anomalies are classified, and a suitable monitoring process algorithm is proposed to improve the overall monitoring quality, accuracy, and stability by applying AI. We also designed a system to present the heater’s power consumption and the hot-pressing furnace’s fan and visualize the process. Combining artificia

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Trần, Ngọc Trung, Hùng Trường Triệu, Vũ Tùng Trần, Hữu Hải Ngô, and Quang Khoa Đào. "An overview of the application of machine learning in predictive maintenance." Petrovietnam Journal 10 (November 30, 2021): 47–61. http://dx.doi.org/10.47800/pvj.2021.10-05.

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With the rise of industrial artificial intelligence (AI), smart sensing, and the Internet of Things (IoT), companies are learning how to use their data not only for analysing the past but also for predicting the future. Maintenance is a crucial area that can drive significant cost savings and production value around the world. Predictive maintenance (PdM) is a technique that collects, cleans, analyses, and utilises data from various manufacturing and sensing sources like machines usage, operating conditions, and equipment feedback. It applies advanced algorithms to the data, automaticall

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Song, Lin, Liping Wang, Jun Wu, Jianhong Liang, and Zhigui Liu. "Integrating Physics and Data Driven Cyber-Physical System for Condition Monitoring of Critical Transmission Components in Smart Production Line." Applied Sciences 11, no. 19 (2021): 8967. http://dx.doi.org/10.3390/app11198967.

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In response to the lack of a unified cyber–physical system framework, which combined the Internet of Things, industrial big data, and deep learning algorithms for the condition monitoring of critical transmission components in a smart production line. In this study, based on the conceptualization of the layers, a novel five-layer cyber–physical systems framework for smart production lines is proposed. This architecture integrates physics and is data-driven. The smart connection layer collects and transmits data, the physical equation modeling layer converts low-value raw data into high-value f

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Fialho, Beatriz Campos, Ricardo Codinhoto, Márcio Minto Fabricio, et al. "Development of a BIM and IoT-Based Smart Lighting Maintenance System Prototype for Universities’ FM Sector." Buildings 12, no. 2 (2022): 99. http://dx.doi.org/10.3390/buildings12020099.

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Reactive maintenance (RM) is a core service of the operation and maintenance (O&M) phase, the most prolonged and costly within the building lifecycle. RM is characterised by inefficient asset information and communication management, impacting critical FM problems and users’ experience. Building information modelling (BIM) and Internet of things (IoT) has enabled the development of digital twins, moving facilities management (FM) from a reactive approach towards a predictive one. Although previous studies have investigated the application of such technologies to FM, there is a lack of unde

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Bulla, Chetan M., and Mahantesh N. Birje. "Improved Data-Driven Root Cause Analysis in a Fog Computing Environment." International Journal of Intelligent Information Technologies 18, no. 1 (2022): 1–28. http://dx.doi.org/10.4018/ijiit.296238.

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Internet of Things (IoT) and cloud computing are used in many real-time smart applications such as smart health-care, smart traffic, smart city, and smart industries. Fog computing has been introduced as an intermediate layer to reduce communication delay between cloud and IoT Devices. To improve the performance of these smart applications, a predictive maintenance system needs to adopt an anomaly detection and root cause analysis model that helps to resolve anomalies and avoid such anomalies in the future. The state of art work on data-driven root cause analysis suffers from scalability, accu

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Milošević, Mijodrag, Dejan Lukić, Gordana Ostojić, Milovan Lazarević, and Aco Antić. "APPLICATION OF CLOUD-BASED MACHINE LEARNING IN CUTTING TOOL CONDITION MONITORING." Journal of Production Engineering 25, no. 1 (2022): 20–24. http://dx.doi.org/10.24867/jpe-2022-01-020.

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One of the primary technologies in the Industry 4.0 concept refers to Smart maintenance or predictive maintenance that includes continuous or periodic sensor monitoring of physical changes in the condition of manufacturing resources (Condition monitoring). In this way, production delays or failures are timely prevented or minimized. In this context, the paper present a developed cloud-based system for monitoring the condition of cutting tool wear by measuring vibration. This system applies a machine learning method that is integrated within the MS Azure cloud system. The verification was perfo

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

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Abstract It is now imperative to create smart systems that prevent mechanical damage through timely preventive maintenance, particularly in construction projects with strict time schedules and budgets. Construction equipment is the largest capital investment for construction companies. Proper maintenance is of major importance for efficiency, productivity, minimization of equipment costs, and environmental management. The aim of this study is to propose an integrated smart system that will monitor the condition based on productivity of the equipment and will provide diagnostic data, helping to

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Hrúz, Michal, Martin Bugaj, Andrej Novák, Branislav Kandera, and Benedikt Badánik. "The Use of UAV with Infrared Camera and RFID for Airframe Condition Monitoring." Applied Sciences 11, no. 9 (2021): 3737. http://dx.doi.org/10.3390/app11093737.

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The new progressive smart technologies announced in the fourth industrial revolution in aviation—Aviation 4.0—represent new possibilities and big challenges in aircraft maintenance processes. The main benefit of these technologies is the possibility to monitor, transfer, store, and analyze huge datasets. Based on analysis outputs, there is a possibility to improve current preventive maintenance processes and implement predictive maintenance processes. These solutions lower the downtime, save manpower, and extend the components’ lifetime; thus, the maximum effectivity and safety is achieved. Th

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Chindanonda, Peeranut, Vladimir Podolskiy, and Michael Gerndt. "Self-Adaptive Data Processing to Improve SLOs for Dynamic IoT Workloads." Computers 9, no. 1 (2020): 12. http://dx.doi.org/10.3390/computers9010012.

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Internet of Things (IoT) covers scenarios of cyber–physical interaction of smart devices with humans and the environment and, such as applications in smart city, smart manufacturing, predictive maintenance, and smart home. Traditional scenarios are quite static in the sense that the amount of supported end nodes, as well as the frequency and volume of observations transmitted, does not change much over time. The paper addresses the challenge of adapting the capacity of the data processing part of IoT pipeline in response to dynamic workloads for centralized IoT scenarios where the quality of u

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Hassankhani Dolatabadi, Sepideh, and Ivana Budinska. "Systematic Literature Review Predictive Maintenance Solutions for SMEs from the Last Decade." Machines 9, no. 9 (2021): 191. http://dx.doi.org/10.3390/machines9090191.

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Today, small- and medium-sized enterprises (SMEs) play an important role in the economy of societies. Although environmental factors, such as COVID-19, as well as non-environmental factors, such as equipment failure, make these industries more vulnerable, they can be minimized by better understanding the concerns and threats these industries face. Only a few SMEs have the capacity to implement the innovative manufacturing technologies of Industry 4.0. The system must be highly adaptable to any equipment, have low costs, avoid the need of doing complex integrations and setups, and have future r

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Macieira, Pedro, Luis Gomes, and Zita Vale. "Energy Management Model for HVAC Control Supported by Reinforcement Learning." Energies 14, no. 24 (2021): 8210. http://dx.doi.org/10.3390/en14248210.

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Heating, ventilating, and air conditioning (HVAC) units account for a significant consumption share in buildings, namely office buildings. Therefore, this paper addresses the possibility of having an intelligent and more cost-effective solution for the management of HVAC units in office buildings. The method applied in this paper divides the addressed problem into three steps: (i) the continuous acquisition of data provided by an open-source building energy management systems, (ii) the proposed learning and predictive model able to predict if users will be working in a given location, and (iii

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ZERMANE, Hanane, Hassina MADJOUR, and Mohammed Adnane BOUZGHAYA. "Prediction of the Amount of Raw Material in an Algerian Cement Factory." Eurasia Proceedings of Science Technology Engineering and Mathematics 19 (December 14, 2022): 41–46. http://dx.doi.org/10.55549/epstem.1218718.

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Factories are currently confronted with multifaceted challenges created by rapid technological Many technologies have recently appeared and evolved, including Cyber-Physical Systems, the Internet of Things, Big Data, and Artificial Intelligence. Companies established various innovative and operational strategies, there is increasing competitiveness among them and increasing companies’ value. A smart factory has emerged as a new industrialization concept that exploits these new technologies to improve the performance, quality, controllability, and transparency of manufacturing processes. Artifi

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Sahal, Radhya, Saeed H. Alsamhi, Kenneth N. Brown, Donna O’Shea, Conor McCarthy, and Mohsen Guizani. "Blockchain-Empowered Digital Twins Collaboration: Smart Transportation Use Case." Machines 9, no. 9 (2021): 193. http://dx.doi.org/10.3390/machines9090193.

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Digital twins (DTs) is a promising technology in the revolution of the industry and essential for Industry 4.0. DTs play a vital role in improving distributed manufacturing, providing up-to-date operational data representation of physical assets, supporting decision-making, and avoiding the potential risks in distributed manufacturing systems. Furthermore, DTs need to collaborate within distributed manufacturing systems to predict the risks and reach consensus-based decision-making. However, DTs collaboration suffers from single failure due to attack and connection in a centralized manner, dat

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Gascón, Alberto, Roberto Casas, David Buldain, and Álvaro Marco. "Providing Fault Detection from Sensor Data in Complex Machines That Build the Smart City." Sensors 22, no. 2 (2022): 586. http://dx.doi.org/10.3390/s22020586.

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Household appliances, climate control machines, vehicles, elevators, cash counting machines, etc., are complex machines with key contributions to the smart city. Those devices have limited memory and processing power, but they are not just actuators; they embed tens of sensors and actuators managed by several microcontrollers and microprocessors communicated by control buses. On the other hand, predictive maintenance and the capability of identifying failures to avoid greater damage of machines is becoming a topic of great relevance in Industry 4.0, and the large amount of data to be processed

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Bezerra, Fábio Vinicius Vieira, Gervásio Protásio Santos Cavalcante, Fabrício Jose Brito Barros, Maria Emília Lima Tostes, and Ubiratan Holanda Bezerra. "Methodology for Predictive Assessment of Failures in Power Station Electric Bays Using the Load Current Frequency Spectrum." Energies 13, no. 19 (2020): 5123. http://dx.doi.org/10.3390/en13195123.

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This paper presents a novel analysis methodology to detect degradation in electrical contacts, with the main goal of implanting a predictive maintenance procedure for sectionalizing switches, circuit breakers, and current transformers in bays of electric transmission and distribution substations. The main feature of the proposed methodology is that it will produce a predictive failure indication for the system under operation, based on the spectral analysis of the load current that is flowing through the bay’s components, using a defined relationship similar to the signal-to-noise ratio (SNR)

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