Academic literature on the topic 'Smart predictive maintenance'
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Journal articles on the topic "Smart predictive maintenance"
Lmouatassime, Abdessalam, and Mohammed Bousmah. "Machine Learning for Predictive Maintenance with Smart Maintenance Simulator." International Journal of Computer Applications 183, no. 22 (August 18, 2021): 35–40. http://dx.doi.org/10.5120/ijca2021921590.
Full textNazara, Krisman Yusuf. "Perancangan Smart Predictive Maintenance untuk Mesin Produksi." Seminar Nasional Official Statistics 2022, no. 1 (November 1, 2022): 691–702. http://dx.doi.org/10.34123/semnasoffstat.v2022i1.1575.
Full textTichý, Tomáš, Jiří Brož, Zuzana Bělinová, and Rastislav Pirník. "Analysis of Predictive Maintenance for Tunnel Systems." Sustainability 13, no. 7 (April 2, 2021): 3977. http://dx.doi.org/10.3390/su13073977.
Full textPech, Martin, Jaroslav Vrchota, and Jiří Bednář. "Predictive Maintenance and Intelligent Sensors in Smart Factory: Review." Sensors 21, no. 4 (February 20, 2021): 1470. http://dx.doi.org/10.3390/s21041470.
Full textDosluoglu, Taner, and Martin MacDonald. "Circuit Design for Predictive Maintenance." Advances in Artificial Intelligence and Machine Learning 02, no. 04 (2022): 533–39. http://dx.doi.org/10.54364/aaiml.2022.1136.
Full textMahmoud, 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 (August 18, 2021): 5078. http://dx.doi.org/10.3390/en14165078.
Full textRaco, 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.
Full textÇınar, 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 (October 5, 2020): 8211. http://dx.doi.org/10.3390/su12198211.
Full textAbdallah, Mustafa, Byung-Gun Joung, Wo Jae Lee, Charilaos Mousoulis, Nithin Raghunathan, Ali Shakouri, John W. Sutherland, and Saurabh Bagchi. "Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets." Sensors 23, no. 1 (January 2, 2023): 486. http://dx.doi.org/10.3390/s23010486.
Full textChen, 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 (March 9, 2022): 2106. http://dx.doi.org/10.3390/s22062106.
Full textDissertations / Theses on the topic "Smart predictive maintenance"
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.
Full textShaif, 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.
Full textSavinov, 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.
Full textCao, 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.
Full textIn 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
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.
Find full textLieti, Valerio. "Development of an Industrial IoT End-to-End Use Case." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Find full textPalesi, Luciano Alessandro Ipsaro. "Human Centered Big Data Analytics for Smart Applications." Doctoral thesis, 2022. http://hdl.handle.net/2158/1282883.
Full textVarwig, 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.
Full textBahooToroody, Ahmad. "Dynamic Risk-based Asset Integrity Modelling of Engineering Processes." Doctoral thesis, 2020. http://hdl.handle.net/2158/1197374.
Full text(10695907), Wo Jae Lee. "AI-DRIVEN PREDICTIVE WELLNESS OF MECHANICAL SYSTEMS: ASSESSMENT OF TECHNICAL, ENVIRONMENTAL, AND ECONOMIC PERFORMANCE." Thesis, 2021.
Find full textOne 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.
Books on the topic "Smart predictive maintenance"
O'Mahony, Niamh, Tania Cerquitelli, Nikolaos Nikolakis, Enrico Macii, and Massimo Ippolito. Predictive Maintenance in Smart Factories: Architectures, Methodologies, and Use-Cases. Springer, 2022.
Find full textO'Mahony, Niamh, Tania Cerquitelli, Nikolaos Nikolakis, Enrico Macii, and Massimo Ippolito. Predictive Maintenance in Smart Factories: Architectures, Methodologies, and Use-Cases. Springer Singapore Pte. Limited, 2021.
Find full textTechnische Zuverlässigkeit 2021. VDI Verlag, 2021. http://dx.doi.org/10.51202/9783181023778.
Full textBook chapters on the topic "Smart predictive maintenance"
Permin, Eike, Florian Lindner, Kevin Kostyszyn, Dennis Grunert, Karl Lossie, Robert Schmitt, and Martin Plutz. "Smart Devices in Production System Maintenance." In Predictive Maintenance in Dynamic Systems, 25–51. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_2.
Full textTorim, Ants, Innar Liiv, Chahinez Ounoughi, and Sadok Ben Yahia. "Pattern Based Software Architecture for Predictive Maintenance." In 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.
Full textChang, Tsung-Yuan, Wei-Ting Cho, Shau-Yin Tseng, Yeni Ouyang, and Chin-Feng Lai. "Predictive Maintenance of Water Purification Unit for Smart Factories." In Communications in Computer and Information Science, 62–70. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6113-9_8.
Full textMahmud, Ismaila, Idris Ismail, and Zuhairi Baharudin. "Predictive Maintenance for a Turbofan Engine Using Data Mining." In 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.
Full textHoffmann, Marcel André. "Case Study Validation of a Predictive Maintenance Implementation Framework." In 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.
Full textPalembiya, Revi Asprila, Muhammad Nanda Setiawan, Elnora Oktaviyani Gultom, Adila Sekarratri Dwi Prayitno, Nani Kurniati, and Mohammad Iqbal. "A Smart Predictive Maintenance Scheme for Classifying Diagnostic and Prognostic Statuses." In Communications in Computer and Information Science, 104–17. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7334-4_8.
Full textHoffmann, Marcel André, and Rainer Lasch. "Roadmap for a Successful Implementation of a Predictive Maintenance Strategy." In 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.
Full textSharanya, S., and S. Karthikeyan. "Local Time Invariant Learning from Industrial Big Data for Predictive Maintenance in Smart Manufacturing." In Big Data Analytics in Smart Manufacturing, 69–85. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003202776-4.
Full textGuerroum, Mariya, Mourad Zegrari, and AbdelHafid Ait Elmahjoub. "Smart Greasing System in Mining Facilities: Proactive and Predictive Maintenance Case Study." In 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.
Full textMay, Gökan, Nikos Kyriakoulis, Konstantinos Apostolou, Sangje Cho, Konstantinos Grevenitis, Stefanos Kokkorikos, Jovana Milenkovic, and Dimitris Kiritsis. "Predictive Maintenance Platform Based on Integrated Strategies for Increased Operating Life of Factories." In 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.
Full textConference papers on the topic "Smart predictive maintenance"
Kshirsagar, Abhay, and Neeraj Patil. "IoT based smart lock with predictive maintenance." In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2021. http://dx.doi.org/10.1109/icccnt51525.2021.9579965.
Full textTichy, Tomas, Jiri Broz, Zuzana Belinova, and Petr Kouba. "Predictive diagnostics usage for telematic systems maintenance." In 2020 Smart City Symposium Prague (SCSP). IEEE, 2020. http://dx.doi.org/10.1109/scsp49987.2020.9134051.
Full textRastogi, Vrinda, Sahima Srivastava, Manasi Mishra, and Rachit Thukral. "Predictive Maintenance for SME in Industry 4.0." In 2020 Global Smart Industry Conference (GloSIC). IEEE, 2020. http://dx.doi.org/10.1109/glosic50886.2020.9267844.
Full textHim, Leong Chee, Yu Yong Poh, and Lee Wah Pheng. "IoT-based Predictive Maintenance for Smart Manufacturing Systems." In 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.
Full textPoor, P., J. Basl, and D. Zenisek. "Predictive Maintenance 4.0 as next evolution step in industrial maintenance development." In 2019 International Research Conference on Smart Computing and Systems Engineering (SCSE). IEEE, 2019. http://dx.doi.org/10.23919/scse.2019.8842659.
Full textMammadov, Emin, Mostafa Farrokhabadi, and Claudio A. Canizares. "AI-enabled Predictive Maintenance of Wind Generators." In 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe). IEEE, 2021. http://dx.doi.org/10.1109/isgteurope52324.2021.9640162.
Full textMeira, Jorge, Eugenia Pérez Pons, Javier Parra Domínguez, Goreti Marreiros, and Carlos Ramos. "PREDICTIVE MAINTENANCE THROUGH DATA-DRIVEN APPROACHES." In 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.
Full textMeesublak, Koonlachat, and Tosapol Klinsukont. "A Cyber-Physical System Approach for Predictive Maintenance." In 2020 IEEE International Conference on Smart Internet of Things (SmartIoT). IEEE, 2020. http://dx.doi.org/10.1109/smartiot49966.2020.00061.
Full textRighetto, Sophia Boing, Marcos A. Izumida Martins, Edgar Gerevini Carvalho, Leandro Takeshi Hattori, and Silvia De Francisci. "Predictive Maintenance 4.0 Applied in Electrical Power Systems." In 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE, 2021. http://dx.doi.org/10.1109/isgt49243.2021.9372230.
Full textAlameldin, Magdi. "Smart Predictive Maintenance Framework SPMF for Gas and Oil Industry." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22497-ms.
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