Bibliographies thématiques / Smart predictive maintenance

Littérature scientifique sur le sujet « Smart predictive maintenance »

Auteur : Grafiati
Publié le 10 mars 2023

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Articles de revues sur le sujet "Smart predictive maintenance"

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Lmouatassime, Abdessalam, et Mohammed Bousmah. « Machine Learning for Predictive Maintenance with Smart Maintenance Simulator ». International Journal of Computer Applications 183, no 22 (18 août 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 (1 novembre 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 et Rastislav Pirník. « Analysis of Predictive Maintenance for Tunnel Systems ». Sustainability 13, no 7 (2 avril 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 et Jiří Bednář. « Predictive Maintenance and Intelligent Sensors in Smart Factory : Review ». Sensors 21, no 4 (20 février 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, et 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 et 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 (18 août 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 et 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 (2 décembre 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 et Babak Safaei. « Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0 ». Sustainability 12, no 19 (5 octobre 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, Charilaos Mousoulis, Nithin Raghunathan, Ali Shakouri, John W. Sutherland et Saurabh Bagchi. « Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets ». Sensors 23, no 1 (2 janvier 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 et Wenlong Li. « Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors ». Sensors 22, no 6 (9 mars 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.
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Thèses sur le sujet "Smart predictive maintenance"

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Ayandokun, O. K. « The incremental motion encoder : a sensor for the integrated condition monitoring of rotating machinery ». Thesis, Nottingham Trent University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245075.

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Shaif, Ayad. « Predictive Maintenance in Smart Agriculture Using Machine Learning : A Novel Algorithm for Drift Fault Detection in Hydroponic Sensors ». Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42270.

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The success of Internet of Things solutions allowed the establishment of new applications such as smart hydroponic agriculture. One typical problem in such an application is the rapid degradation of the deployed sensors. Traditionally, this problem is resolved by frequent manual maintenance, which is considered to be ineffective and may harm the crops in the long run. The main purpose of this thesis was to propose a machine learning approach for automating the detection of sensor fault drifts. In addition, the solution’s operability was investigated in a cloud computing environment in terms of the response time. This thesis proposes a detection algorithm that utilizes RNN in predicting sensor drifts from time-series data streams. The detection algorithm was later named; Predictive Sliding Detection Window (PSDW) and consisted of both forecasting and classification models. Three different RNN algorithms, i.e., LSTM, CNN-LSTM, and GRU, were designed to predict sensor drifts using forecasting and classification techniques. The algorithms were compared against each other in terms of relevant accuracy metrics for forecasting and classification. The operability of the solution was investigated by developing a web server that hosted the PSDW algorithm on an AWS computing instance. The resulting forecasting and classification algorithms were able to make reasonably accurate predictions for this particular scenario. More specifically, the forecasting algorithms acquired relatively low RMSE values as ~0.6, while the classification algorithms obtained an average F1-score and accuracy of ~80% but with a high standard deviation. However, the response time was ~5700% slower during the simulation of the HTTP requests. The obtained results suggest the need for future investigations to improve the accuracy of the models and experiment with other computing paradigms for more reliable deployments.
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Savinov, Valeriy. « Smart city platforms : designing a module to visualize information for real estate companies ». Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-77846.

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This thesis is a study with focus on real estate companies for one of several sub-projects under “Stadens kontrollrum” initiative in Västerås. “Stadens kontrollrum” is a concept that brought together expertise from various fields of industry, research and government to create a platform that will aggregate data from different stakeholders and proposed services to achieve the goal of making Västerås a smart and sustainable city. Our project aims to extend “Stadens kontrollrum” platform in order to make it beneficial for real estate companies. In this case study, we applied expert driven methodology, i.e. with domain experts. A detailed literature review has been performed. We identified user requirements based on the information gathered during workshops with nine participants from real estate and utility companies; interviews with three experts from Mälarenergi. During the study, we identified that data visualisation, predictive maintenance and big data analysis for decision making are the main tools, among others, that should be applied to facilitate user needs. Based on user requirements, we have suggested an architecture of a module for the “Stadens kontrollrum” platform that includes those features. To verify feasibility of the solution, a prototype was built and evaluated with a group of four experts from Mälarenergi. The prototype is going to serve as a live demo in workshops and further discussions with the potential users later in the project. A full prototype of the solution is planned to be implemented in the next stage of the project.
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Cao, Qiushi. « Semantic technologies for the modeling of predictive maintenance for a SME network in the framework of industry 4.0 Smart condition monitoring for industry 4.0 manufacturing processes : an ontology-based approach Using rule quality measures for rule base refinement in knowledge-based predictive maintenance systems Combining chronicle mining and semantics for predictive maintenance in manufacturing processes ». Thesis, Normandie, 2020. http://www.theses.fr/2020NORMIR04.

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Dans le domaine de la fabrication, la détection d’anomalies telles que les défauts et les défaillances mécaniques permet de lancer des tâches de maintenance prédictive, qui visent à prévoir les défauts, les erreurs et les défaillances futurs et à permettre des actions de maintenance. Avec la tendance de l’industrie 4.0, les tâches de maintenance prédictive bénéficient de technologies avancées telles que les systèmes cyberphysiques (CPS), l’Internet des objets (IoT) et l’informatique dématérialisée (cloud computing). Ces technologies avancées permettent la collecte et le traitement de données de capteurs qui contiennent des mesures de signaux physiques de machines, tels que la température, la tension et les vibrations. Cependant, en raison de la nature hétérogène des données industrielles, les connaissances extraites des données industrielles sont parfois présentées dans une structure complexe. Des méthodes formelles de représentation des connaissances sont donc nécessaires pour faciliter la compréhension et l’exploitation des connaissances. En outre, comme les CPSs sont de plus en plus axées sur la connaissance, une représentation uniforme de la connaissance des ressources physiques et des capacités de raisonnement pour les tâches analytiques est nécessaire pour automatiser les processus de prise de décision dans les CPSs. Ces problèmes constituent des obstacles pour les opérateurs de machines qui doivent effectuer des opérations de maintenance appropriées. Pour relever les défis susmentionnés, nous proposons dans cette thèse une nouvelle approche sémantique pour faciliter les tâches de maintenance prédictive dans les processus de fabrication. En particulier, nous proposons quatre contributions principales: i) un cadre ontologique à trois niveaux qui est l’élément central d’un système de maintenance prédictive basé sur la connaissance; ii) une nouvelle approche sémantique hybride pour automatiser les tâches de prédiction des pannes de machines, qui est basée sur l’utilisation combinée de chroniques (un type plus descriptif de modèles séquentiels) et de technologies sémantiques; iii) a new approach that uses clustering methods with Semantic Web Rule Language (SWRL) rules to assess failures according to their criticality levels; iv) une nouvelle approche d’affinement de la base de règles qui utilise des mesures de qualité des règles comme références pour affiner une base de règles dans un système de maintenance prédictive basé sur la connaissance. Ces approches ont été validées sur des ensembles de données réelles et synthétiques
In the manufacturing domain, the detection of anomalies such as mechanical faults and failures enables the launching of predictive maintenance tasks, which aim to predict future faults, errors, and failures and also enable maintenance actions. With the trend of Industry 4.0, predictive maintenance tasks are benefiting from advanced technologies such as Cyber-Physical Systems (CPS), the Internet of Things (IoT), and Cloud Computing. These advanced technologies enable the collection and processing of sensor data that contain measurements of physical signals of machinery, such as temperature, voltage, and vibration. However, due to the heterogeneous nature of industrial data, sometimes the knowledge extracted from industrial data is presented in a complex structure. Therefore formal knowledge representation methods are required to facilitate the understanding and exploitation of the knowledge. Furthermore, as the CPSs are becoming more and more knowledge-intensive, uniform knowledge representation of physical resources and reasoning capabilities for analytic tasks are needed to automate the decision-making processes in CPSs. These issues bring obstacles to machine operators to perform appropriate maintenance actions. To address the aforementioned challenges, in this thesis, we propose a novel semantic approach to facilitate predictive maintenance tasks in manufacturing processes. In particular, we propose four main contributions: i) a three-layered ontological framework that is the core component of a knowledge-based predictive maintenance system; ii) a novel hybrid semantic approach to automate machinery failure prediction tasks, which is based on the combined use of chronicles (a more descriptive type of sequential patterns) and semantic technologies; iii) a new approach that uses clustering methods with Semantic Web Rule Language (SWRL) rules to assess failures according to their criticality levels; iv) a novel rule base refinement approach that uses rule quality measures as references to refine a rule base within a knowledge-based predictive maintenance system. These approaches have been validated on both real-world and synthetic data sets
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Barbieri, Matteo. « Seamless infrastructure for "Big-Data" collection and transportation and distributed elaboration oriented to predictive maintenance of automatic machines ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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In questo progetto di tesi, realizzato all'interno del laboratorio industriale LIAM Lab, si propone lo sviluppo e la sperimentazione di un'infrastruttura hardware e software per l'acquisizione e l'elaborazione di segnali da sensori di macchine automatiche da utilizzare per effettuare operazioni di diagnostica predittiva su di essa. La tematica sta avendo sempre più seguito all'interno del settore, in quanto la sua realizzazione si basa profondamente sui concetti di industria 4.0, internet delle cose e big data. Nel caso particolare l'infrastruttura riceverà dati da accelerometri con frequenze variabli dai 5KHz a 50KHz e su di questi applicherà un algoritmo di identificazione e semplici test statistici. Successivamente, i parametri dentificati e i risultati dei test verranno poi inviati via OPC ad un computer che provvederà alla loro rielaborazione. Con rielaborazione si intende l'utilizzo di ulteriori test statistici più complessi e anche algoritmi di machine learning. L'infrastruttura ha quindi il compito di "prepare la strada" per l'acquisizione e rielaborazione dei segnali ricevuti dai sensori per poter realizzare in seguito algoritmi in grado di apprendere le condizioni operative della macchina cosicchè sia possibile prevederne produzione e manutenzione.
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Lieti, Valerio. « Development of an Industrial IoT End-to-End Use Case ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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This project is born merging my personal interest in IoT and AI fields and the collaboration with WiLab, Bi-rex and EBWorld. By joining different skills, the final result is the realization of an end-to-end Industrial IoT application. This IIoT use-case focuses on monitoring accelerations that characterize a turntable, on which mechanical parts are processed. The purpose of the monitoring is to implement predictive maintenance to report potential malfunctions. The turntable in question belongs to DMG Mori, a five-axis turning and milling machine for subtractive manufacturing, machine located at Bi-Rex. Bi-rex is a national competence center introduced by the Ministry of the Economic Development, focused on Big Data and Industry 4.0. It is a public-private consortium aimed to deal with digital transformation and technological innovation, based in Bologna. The technology applied for monitoring is LoRa at 2.4 GHz and the network implemented is a tree topology system of WiLab property, called IMMUNeT (Industrial Machine Monitoring Unplugged Network). Beginning from the adaptation of the accelerometer firmware, moving to the dispatching of extracted data to the server, developing machine learning techniques aimed at predictive maintenance, I lastly exhibit the operating state through the Node-RED dashboard. The resulting data describes if it is required to take actions on the machine or to stop it, in order to avoid a dangerous situation or crack the machinery. This critical information is shown graphically on a 3D interactive map to ensure a more user-friendly interface.
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Palesi, Luciano Alessandro Ipsaro. « Human Centered Big Data Analytics for Smart Applications ». Doctoral thesis, 2022. http://hdl.handle.net/2158/1282883.

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This thesis is concerned with Smart Applications. Smart applications are all those applications that incorporate data-driven, actionable insights in the user experience, and they allow in different contexts users to complete actions or make decisions efficiently. The differences between smart applications and traditional applications are mainly that the former are dynamic and evolve on the basis of intuition, user feedback or new data. Moreover, smart applications are data-driven and linked to the context of use. There are several aspects to be considered in the development of intelligent applications, such as machine learning algorithms for producing insights, privacy, data security and ethics. The purpose of this thesis is to study and develop human centered algorithms and systems in different contexts (retail, industry, environment and smart city) with particular attention to big data analysis and prediction techniques. The second purpose of this thesis is to study and develop techniques for the interpretation of results in order to make artificial intelligence algorithms "explainable". Finally, the third and last purpose is to develop solutions in GDPR compliant environments and then secure systems that respect user privacy.
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Varwig, Andreas Werner. « Smart Enterprise Analytics - Evaluation, Adaption und Implementierung von Analyseverfahren zur Automatisierung des Informationsmanagements ». Doctoral thesis, 2018. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-20181004609.

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Die Identifikation von flexibel einsetzbaren, mächtigen Verfahren zur Massendatenanalyse und die Schaffung von standardisierbaren Vorgehensmodellen zur Integration dieser Verfahren in IT-Systeme sind zentrale Herausforderungen für die moderne Wirtschaftsinformatik. Insbesondere für KMU ist die Entwicklung standardisierter Lösungsansätze von großer Relevanz. Dies gilt über alle Branchen. Finanzdienstleister sind ebenso betroffen wie der Maschinen- und Anlagenbau. Im Rahmen dieser Forschungsarbeit wird eine Wissensbasis geschaffen werden, welche es einer breiten Masse an Unternehmen ermöglicht, geeignete quantitative Methoden zur Datenanalyse zu erkennen und diese für sich nutzbar zu machen.
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BahooToroody, Ahmad. « Dynamic Risk-based Asset Integrity Modelling of Engineering Processes ». Doctoral thesis, 2020. http://hdl.handle.net/2158/1197374.

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Poorly maintenance scheduling and the resulting downtime are costly. Deliver maximum performance while minimizing costs and risks over the whole life of engineering systems required a developmental transition from traditional maintenance strategies to smart predictive maintenance. This PhD study aimed at maximizing the value realized from complex engineering assets and systems. To this end, statistical modelling and machine learning were established to problems in intelligent maintenance operations, characterized by data-driven innovations.
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(10695907), Wo Jae Lee. « AI-DRIVEN PREDICTIVE WELLNESS OF MECHANICAL SYSTEMS : ASSESSMENT OF TECHNICAL, ENVIRONMENTAL, AND ECONOMIC PERFORMANCE ». Thesis, 2021.

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One way to reduce the lifecycle cost and environmental impact of a product in a circular economy is to extend its lifespan by either creating longer-lasting products or managing the product properly during its use stage. Life extension of a product is envisioned to help better utilize raw materials efficiently and slow the rate of resource depletion. In the case of manufacturing equipment (e.g., an electric motor on a machine tool), securing reliable service life as well as the life extension are important for consistent production and operational excellence in a factory. However, manufacturing equipment is often utilized without a planned maintenance approach. Such a strategy frequently results in unplanned downtime, owing to unexpected failures. Scheduled maintenance replaces components frequently to avoid unexpected equipment stoppages, but increases the time associated with machine non-operation and maintenance cost.


Recently, the emergence of Industry 4.0 and smart systems is leading to increasing attention to predictive maintenance (PdM) strategies that can decrease the cost of downtime and increase the availability (utilization rate) of manufacturing equipment. PdM also has the potential to foster sustainable practices in manufacturing by maximizing the useful lives of components. In addition, advances in sensor technology (e.g., lower fabrication cost) enable greater use of sensors in a factory, which in turn is producing greater and more diverse sets of data. Widespread use of wireless sensor networks (WSNs) and plug-and-play interfaces for the data collection on product/equipment states are allowing predictive maintenance on a much greater scale. Through advances in computing, big data analysis is faster/improved and has allowed maintenance to transition from run-to-failure to statistical inference-based or machine learning prediction methods.


Moreover, maintenance practice in a factory is evolving from equipment “health management” to equipment “wellness” by establishing an integrated and collaborative manufacturing system that responds in real-time to changing conditions in a factory. The equipment wellness is an active process of becoming aware of the health condition and of making choices that achieve the full potential of the equipment. In order to enable this, a large amount of machine condition data obtained from sensors needs to be analyzed to diagnose the current health condition and predict future behavior (e.g., remaining useful life). If a fault is detected during this diagnosis, a root cause of a fault must be identified to extend equipment life and prevent problem reoccurrence.


However, it is challenging to build a model capturing a relationship between multi-sensor signals and mechanical failures, considering the dynamic manufacturing environment and the complex mechanical system in equipment. Another key challenge is to obtain usable machine condition data to validate a method.


A goal of the proposed work is to develop a systematic tool for maintenance in manufacturing plants using emerging technologies (e.g., AI, Smart Sensor, and IoT). The proposed method will facilitate decision-making that supports equipment maintenance by rapidly detecting a worn component and estimating remaining useful life. In order to diagnose and prognose a health condition of equipment, several data-driven models that describe the relationships between proxy measures (i.e., sensor signals) and machine health conditions are developed and validated through the experiment for several different manufacturing-oriented cases (e.g., cutting tool, gear, and bearing). To enhance the robustness and the prediction capability of the data-driven models, signal processing is conducted to preprocess the raw signals using domain knowledge. Through this process, useful features from the large dataset are extracted and selected, thus increasing computational efficiency in model training. To make a decision using the processed signals, a customized deep learning architecture for each case is designed to effectively and efficiently learn the relationship between the processed signals and the model’s outputs (e.g., health indicators). Ultimately, the method developed through this research helps to avoid catastrophic mechanical failures, products with unacceptable quality, defective products in the manufacturing process as well as to extend equipment service life.


To summarize, in this dissertation, the assessment of technical, environmental and economic performance of the AI-driven method for the wellness of mechanical systems is conducted. The proposed methods are applied to (1) quantify the level of tool wear in a machining process, (2) detect different faults from a power transmission mini-motor testbed (CNN), (3) detect a fault in a motor operated under various rotation speeds, and (4) to predict the time to failure of rotating machinery. Also, the effectiveness of maintenance in the use stage is examined from an environmental and economic perspective using a power efficiency loss as a metric for decision making between repair and replacement.


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Livres sur le sujet "Smart predictive maintenance"

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O'Mahony, Niamh, Tania Cerquitelli, Nikolaos Nikolakis, Enrico Macii et Massimo Ippolito. Predictive Maintenance in Smart Factories : Architectures, Methodologies, and Use-Cases. Springer, 2022.

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O'Mahony, Niamh, Tania Cerquitelli, Nikolaos Nikolakis, Enrico Macii et Massimo Ippolito. Predictive Maintenance in Smart Factories : Architectures, Methodologies, and Use-Cases. Springer Singapore Pte. Limited, 2021.

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Technische Zuverlässigkeit 2021. VDI Verlag, 2021. http://dx.doi.org/10.51202/9783181023778.

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Aus dem Vorwort: Durch die zunehmende Digitalisierung und Vernetzung, beispielsweise in einer Smart Factory im Kontext von Industrie 4.0, werden hohe Anforderungen an die Zuverlässigkeit, die Verfügbarkeit und die Sicherheit von Maschinen und Anlagen gestellt. Dies erfordert den konsequenten Einsatz und die ständige Weiterentwicklung von Methoden und Modellen der Zuverlässigkeitstechnik entlang des gesamten Lebenszyklus zur Planung, Entwicklung und Absicherung der Zuverlässigkeit. Die zunehmende Digitalisierung bietet durch die steigende Zugänglichkeit und Verfügbarkeit von relevanten Daten gleichzeitig enorme Chancen und neue Möglichkeiten für die Anwendung dieser Methoden und Modelle für Zuverlässigkeitsanalysen und -prognosen. Inhalt Prognostics and Health Management (PHM) und Industrie 4.0 Restlebensdauervorhersage für Filtrationssysteme mittels Random Forest ..... 3 Untersuchung von Datensätzen und Definition praxisrelevanter Standardfälle im Kontext von Predictive Maintenance ..... 17 Methodik zur Schadensquantifizierung in hydraulischen Axialkolbeneinheiten unter variablen Betriebsbedingungen ..... 33 ...
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Chapitres de livres sur le sujet "Smart predictive maintenance"

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Permin, Eike, Florian Lindner, Kevin Kostyszyn, Dennis Grunert, Karl Lossie, Robert Schmitt et Martin Plutz. « Smart Devices in Production System Maintenance ». Dans Predictive Maintenance in Dynamic Systems, 25–51. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_2.

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Torim, Ants, Innar Liiv, Chahinez Ounoughi et Sadok Ben Yahia. « Pattern Based Software Architecture for Predictive Maintenance ». Dans Communications in Computer and Information Science, 26–38. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17030-0_3.

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AbstractMany industrial sectors are moving toward Industry Revolution (IR) 4.0. In this respect, the Internet of Things and predictive maintenance are considered the key pillars of IR 4.0. Predictive maintenance is one of the hottest trends in manufacturing where maintenance work occurs according to continuous monitoring using a healthiness check for processing equipment or instrumentation. It enables the maintenance team to have an advanced prediction of failures and allows the team to undertake timely corrective actions and decisions ahead of time. The aim of this paper is to present a smart monitoring and diagnostics system as an expert system that can alert an operator before equipment failures to prevent material and environmental damages. The main novelty and contribution of this paper is a flexible architecture of the predictive maintenance system, based on software patterns - flexible solutions to general problems. The presented conceptual model enables the integration of an expert knowledge of anticipated failures and the matrix-profile technique based anomaly detection. The results so far are encouraging.
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Chang, Tsung-Yuan, Wei-Ting Cho, Shau-Yin Tseng, Yeni Ouyang et Chin-Feng Lai. « Predictive Maintenance of Water Purification Unit for Smart Factories ». Dans Communications in Computer and Information Science, 62–70. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6113-9_8.

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Mahmud, Ismaila, Idris Ismail et Zuhairi Baharudin. « Predictive Maintenance for a Turbofan Engine Using Data Mining ». Dans International Conference on Artificial Intelligence for Smart Community, 677–87. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-2183-3_65.

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Hoffmann, Marcel André. « Case Study Validation of a Predictive Maintenance Implementation Framework ». Dans Smart and Sustainable Supply Chain and Logistics — Challenges, Methods and Best Practices, 49–60. Cham : Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15412-6_5.

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Palembiya, Revi Asprila, Muhammad Nanda Setiawan, Elnora Oktaviyani Gultom, Adila Sekarratri Dwi Prayitno, Nani Kurniati et Mohammad Iqbal. « A Smart Predictive Maintenance Scheme for Classifying Diagnostic and Prognostic Statuses ». Dans Communications in Computer and Information Science, 104–17. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7334-4_8.

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Hoffmann, Marcel André, et Rainer Lasch. « Roadmap for a Successful Implementation of a Predictive Maintenance Strategy ». Dans Smart and Sustainable Supply Chain and Logistics – Trends, Challenges, Methods and Best Practices, 423–39. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61947-3_29.

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Sharanya, S., et S. Karthikeyan. « Local Time Invariant Learning from Industrial Big Data for Predictive Maintenance in Smart Manufacturing ». Dans Big Data Analytics in Smart Manufacturing, 69–85. Boca Raton : Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003202776-4.

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Guerroum, Mariya, Mourad Zegrari et AbdelHafid Ait Elmahjoub. « Smart Greasing System in Mining Facilities : Proactive and Predictive Maintenance Case Study ». Dans Communications in Computer and Information Science, 348–62. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20490-6_28.

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May, Gökan, Nikos Kyriakoulis, Konstantinos Apostolou, Sangje Cho, Konstantinos Grevenitis, Stefanos Kokkorikos, Jovana Milenkovic et Dimitris Kiritsis. « Predictive Maintenance Platform Based on Integrated Strategies for Increased Operating Life of Factories ». Dans Advances in Production Management Systems. Smart Manufacturing for Industry 4.0, 279–87. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99707-0_35.

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Actes de conférences sur le sujet "Smart predictive maintenance"

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Kshirsagar, Abhay, et Neeraj Patil. « IoT based smart lock with predictive maintenance ». Dans 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2021. http://dx.doi.org/10.1109/icccnt51525.2021.9579965.

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Tichy, Tomas, Jiri Broz, Zuzana Belinova et Petr Kouba. « Predictive diagnostics usage for telematic systems maintenance ». Dans 2020 Smart City Symposium Prague (SCSP). IEEE, 2020. http://dx.doi.org/10.1109/scsp49987.2020.9134051.

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Rastogi, Vrinda, Sahima Srivastava, Manasi Mishra et Rachit Thukral. « Predictive Maintenance for SME in Industry 4.0 ». Dans 2020 Global Smart Industry Conference (GloSIC). IEEE, 2020. http://dx.doi.org/10.1109/glosic50886.2020.9267844.

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Him, Leong Chee, Yu Yong Poh et Lee Wah Pheng. « IoT-based Predictive Maintenance for Smart Manufacturing Systems ». Dans 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2019. http://dx.doi.org/10.1109/apsipaasc47483.2019.9023106.

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Poor, P., J. Basl et D. Zenisek. « Predictive Maintenance 4.0 as next evolution step in industrial maintenance development ». Dans 2019 International Research Conference on Smart Computing and Systems Engineering (SCSE). IEEE, 2019. http://dx.doi.org/10.23919/scse.2019.8842659.

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Mammadov, Emin, Mostafa Farrokhabadi et Claudio A. Canizares. « AI-enabled Predictive Maintenance of Wind Generators ». Dans 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe). IEEE, 2021. http://dx.doi.org/10.1109/isgteurope52324.2021.9640162.

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Meira, Jorge, Eugenia Pérez Pons, Javier Parra Domínguez, Goreti Marreiros et Carlos Ramos. « PREDICTIVE MAINTENANCE THROUGH DATA-DRIVEN APPROACHES ». Dans Proceedings of the III Workshop on Disruptive Information and Communication Technologies for Innovation and Digital Transformation : 18th December 2020 Online. Ediciones Universidad de Salamanca, 2022. http://dx.doi.org/10.14201/0aq03111326.

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Nowadays, the Industrial Internet promises to transform our world. The melding of the global industrial system that was made possible because of the Industrial Revolution, with the open computing and communication systems developed as part of the Internet Revolution, opens new frontiers to accelerate productivity, reduce inefficiency and waste, and enhance the human work experience. With the emergence of Industry 4.0, smart systems, machine learning within artificial intelligence, predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. This paper focus on the PdM field, describing and specifying, its techniques, applications in the real world, methods and approaches widely used as such its challenges.
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Meesublak, Koonlachat, et Tosapol Klinsukont. « A Cyber-Physical System Approach for Predictive Maintenance ». Dans 2020 IEEE International Conference on Smart Internet of Things (SmartIoT). IEEE, 2020. http://dx.doi.org/10.1109/smartiot49966.2020.00061.

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Righetto, Sophia Boing, Marcos A. Izumida Martins, Edgar Gerevini Carvalho, Leandro Takeshi Hattori et Silvia De Francisci. « Predictive Maintenance 4.0 Applied in Electrical Power Systems ». Dans 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE, 2021. http://dx.doi.org/10.1109/isgt49243.2021.9372230.

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Alameldin, Magdi. « Smart Predictive Maintenance Framework SPMF for Gas and Oil Industry ». Dans International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22497-ms.

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Abstract The O&G industry is facing big challenges which consequently raise the necessity for reforming its traditional business model and integrating digital disruptive technologies such as Digital Twins, Artificial Intelligence and Blockchain. A Digital Twin(DT) is defined as a dynamic intelligent digital replica/model of the physical system/process/service/people which enables just-in-time informed decision making and root-cause analysis using AI. DTs are implanted at different levels such as Equipment/Asset Level Twin, System Level Twin, System of Systems (SoS) Level Twin. This research introduces a novel framework which is based on a Smart Secure Digital Twin (S2DT) to bridge the development gap compared to other leading industries such as manufacturing and automotive. The proposed model relies on Tiny Machine Learning (TinyML) to implement edge intelligence and solve the problems of transfer latency and data overload and consequently achieves low carbon footprint. Edge Intelligence (EI) reduces energy consumption and enhances security and perspective maintenance. The Blockchain Technology is used to solve the privacy, and cybersecurity problems [4]. The Extended Reality (XR) will be used to ensure proper training of operators, and industry 5.0 to boost collaboration between human and machine. At the component level, security is maintained by integrated the locally generated intelligence on a blockchain to insure immutability, and enhance security.
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