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Literatura académica sobre el tema "Smart predictive maintenance"
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Artículos de revistas sobre el tema "Smart predictive maintenance"
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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 learning. Model predictive maintenance ini diharapkan mampu memprediksi jadwal pemeliharaan mesin sehingga menambah masa pakai mesin produksi, membantu estimasi biaya pemeliharaan. Metode analisis yang digunakan mengacu pada analisis klasifikasi dengan membandingkan 6 (enam) model klasifikasi, yaitu: XGBoost, k-nearest neighbours, logistic regression, gradient boosting, decision tree regression dan random forest. Perbandingan algoritma tersebut untuk mendapatkan model klasifikasi terbaik pada kasus mesin produksi. Dari enam algoritma tersebut, model terbaik diperoleh dari XG-boost dengan akurasi sebesar 99,07.
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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 is to summarize the possibilities of optimizing system maintenance that are based on data analysis as well as expert analysis based on the experience with the equipment in the tunnel. The results, findings, and conclusions could primarily be used in the tunnels; secondarily, these principles could be applied in telematics and lead to the optimization and improvement of system sustainability.
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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 analysis, systematic review methodology, co-occurrence analysis of keywords, and cluster analysis. The results show the increasing number of papers related to key researched concepts. The importance of predictive maintenance is growing over time in relation to Industry 4.0 technologies. We proposed Smart and Intelligent Predictive Maintenance (SIPM) based on the full-text analysis of relevant papers. The paper’s main contribution is the summary and overview of current trends in intelligent sensors used for predictive maintenance in smart factories.
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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 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.
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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 between 2012 and 2020. Sixty-five (n = 65) were chosen based on specified exclusion and inclusion criteria. Fifty-seven percent (n = 37/65) of the studies analyzed the issues from predictive maintenance perspectives, while about 18% (n = 12/65) focused on factors-related review studies on the smart grid and about 15% (n = 10/65) focused on factors related to the experimental study. The remaining 9% (n = 6/65) concentrated on fields related to the challenges and benefits of the study. The significance of predictive maintenance has been developing over time in connection with Industry 4.0 revolution. The paper’s fundamental commitment is the outline and overview of faults in the smart grid such as fault location and detection. Therefore, advanced methods of applying Artificial Intelligence (AI) techniques can enhance and improve the reliability and resilience of smart grid systems. For future direction, we aim to supply a deep understanding of Smart meters to detect or monitor faults in the smart grid as it is the primary IoT sensor in an AMI.
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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 main challenge. Furthermore, the study of the accessibility and usability of data and information from sources such as the three-dimensional terrestrial survey, existing databases, sensor networks, and satellite technologies make it possible to investigate both different ways of data modelling, even with a view to the development of predictive algorithms, and of visualisation and information management. The study illustrates part of the results of the InSPiRE project, an industrial research project financed with European structural funds and carried out in a public-private partnership by four universities and public research bodies, an innovation centre and six companies, SMEs, large enterprises, and start-ups. Specifically, the project highlights the growing importance of BIM-based modelling as a tool to lead users, both experts and non-experts, through the multiple information paths resulting from the relation between data and metadata.
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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 to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.
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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 each sensor data leveraging deep learning techniques. We also evaluate the performance of several traditional and ML-based forecasting models for predicting the time series of sensor data. We show that careful selection of training data by aggregating multiple predictive RPM values is beneficial. Then, considering the sparse data from one kind of sensor, we perform transfer learning from a high data rate sensor to perform defect type classification. We release our manufacturing database corpus (4 datasets) and codes for anomaly detection and defect type classification for the community to build on it. Taken together, we show that predictive failure classification can be achieved, paving the way for predictive maintenance.
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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 pumps and the characteristics of vibration signals when a fault occurs are introduced. Secondly, the centrifugal pump monitoring IoT system is designed. The system is mainly composed of wireless sensors, wired sensors, data collectors, and cloud servers. Then, the microelectromechanical system (MEMS) chip is used to design a wireless vibration temperature integrated sensor, a wired vibration temperature integrated sensor, and a data collector to monitor the running state of the pump. The designed wireless sensor communicates with the server through Narrow Band Internet of Things (NB-IoT). The output of the wired sensor is connected to the data collector, and the designed collector can communicate with the server through 4G communication. Through cloud-side collaboration, real-time monitoring of the running status of centrifugal pumps and intelligent diagnosis of centrifugal pump faults are realized. Finally, on-site testing and application verification of the system was conducted. The test results show that the designed sensors and sensor application system can make good use of the centrifugal pump failure mechanism to automatically diagnose equipment failures. Moreover, the diagnostic accuracy rate is above 85% by using the method of wired sensor and collector. As a low-cost and easy-to-implement solution, wireless sensors can also monitor gradual failures well. The research on the sensors and pump monitoring system provides feasible methods and an effective means for the application of centrifugal pump health management and predictive maintenance.