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Artykuły w czasopismach na temat "MAINTENANCE PREDICTION"
Marshall, David F. "Language Maintenance and Revival". Annual Review of Applied Linguistics 14 (marzec 1994): 20–33. http://dx.doi.org/10.1017/s0267190500002798.
Pełny tekst źródłaXu, Peng, Rengkui Liu, Quanxin Sun i Futian Wang. "A Novel Short-Range Prediction Model for Railway Track Irregularity". Discrete Dynamics in Nature and Society 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/591490.
Pełny tekst źródłaNansamba, Salmah, i Hadi Harb. "Developing a Neural Network Based Fault Prediction Tool for a Solar Power Plant in Uganda". Transactions on Machine Learning and Artificial Intelligence 10, nr 6 (28.12.2022): 52–70. http://dx.doi.org/10.14738/tmlai.106.13645.
Pełny tekst źródłaKang, Ziqiu, Cagatay Catal i Bedir Tekinerdogan. "Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks". Sensors 21, nr 3 (30.01.2021): 932. http://dx.doi.org/10.3390/s21030932.
Pełny tekst źródłaFitra Azyus, Adryan, Sastra Kusuma Wijaya i Mohd Naved. "Determining RUL Predictive Maintenance on Aircraft Engines Using GRU". Journal of Mechanical, Civil and Industrial Engineering 3, nr 3 (11.12.2022): 79–84. http://dx.doi.org/10.32996/jmcie.2022.3.3.10.
Pełny tekst źródłaD., Ganga, i Ramachandran V. "Adaptive prediction model for effective electrical machine maintenance". Journal of Quality in Maintenance Engineering 26, nr 1 (18.04.2019): 166–80. http://dx.doi.org/10.1108/jqme-12-2017-0087.
Pełny tekst źródłaTong, Guoqiang, Xinbo Qian i Yilai Liu. "Prognostics and Predictive Maintenance Optimization Based on Combination BP-RBF-GRNN Neural Network Model and Proportional Hazard Model". Journal of Sensors 2022 (29.04.2022): 1–17. http://dx.doi.org/10.1155/2022/8655669.
Pełny tekst źródłaRodrigues, Joao, Jose Torres Farinha i Antonio Marques Cardoso. "Predictive Maintenance Tools – A Global Survey". WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 16 (22.01.2021): 96–109. http://dx.doi.org/10.37394/23203.2021.16.7.
Pełny tekst źródłaGibiec, Mariusz. "Prediction of Machines Health with Application of an Intelligent Approach – a Mining Machinery Case Study". Key Engineering Materials 293-294 (wrzesień 2005): 661–68. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.661.
Pełny tekst źródłaZhu, Ya Hong, Ji Ping Cao, Wen Xia Sun, Yang Tao Fan i Zhi Hui Zhao. "Demand Forecasting Model Based on Equipment Maintenance Resources in Virtual Warehousing". Applied Mechanics and Materials 556-562 (maj 2014): 5442–49. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5442.
Pełny tekst źródłaRozprawy doktorskie na temat "MAINTENANCE PREDICTION"
Morrison, David J. "Prediction of software maintenance costs". Thesis, Edinburgh Napier University, 2001. http://researchrepository.napier.ac.uk/Output/3601.
Pełny tekst źródłaIshihara, Yasuo. "Prediction of human error in rail car maintenance". Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10629.
Pełny tekst źródłaHartmann, Jens. "Analysis of maintenance records to support prediction of maintenance requirements in the German Army". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA392054.
Pełny tekst źródłaKumbala, Bharadwaj Reddy. "Predictive Maintenance of NOx Sensor using Deep Learning : Time series prediction with encoder-decoder LSTM". Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18668.
Pełny tekst źródłaPodda, G. "PREDICTION OF OPTIMAL WARFARIN MAINTENANCE DOSE USING ADVANCED ARTIFICIAL NEURAL NETWORKS". Doctoral thesis, Università degli Studi di Milano, 2013. http://hdl.handle.net/2434/219087.
Pełny tekst źródłaTse, Peter W. "Neural networks for machine fault diagnosis and life span prediction". Thesis, University of Sussex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390518.
Pełny tekst źródłaWan, Husain Wan Mohd Sufian Bin. "Maintainability prediction for aircraft mechanical components utilising aircraft feedback information". Thesis, Cranfield University, 2011. http://dspace.lib.cranfield.ac.uk/handle/1826/7272.
Pełny tekst źródłaKaidis, Christos. "Wind Turbine Reliability Prediction : A Scada Data Processing & Reliability Estimation Tool". Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-221135.
Pełny tekst źródłaSammouri, Wissam. "Data mining of temporal sequences for the prediction of infrequent failure events : application on floating train data for predictive maintenance". Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1041/document.
Pełny tekst źródłaIn order to meet the mounting social and economic demands, railway operators and manufacturers are striving for a longer availability and a better reliability of railway transportation systems. Commercial trains are being equipped with state-of-the-art onboard intelligent sensors monitoring various subsystems all over the train. These sensors provide real-time flow of data, called floating train data, consisting of georeferenced events, along with their spatial and temporal coordinates. Once ordered with respect to time, these events can be considered as long temporal sequences which can be mined for possible relationships. This has created a neccessity for sequential data mining techniques in order to derive meaningful associations rules or classification models from these data. Once discovered, these rules and models can then be used to perform an on-line analysis of the incoming event stream in order to predict the occurrence of target events, i.e, severe failures that require immediate corrective maintenance actions. The work in this thesis tackles the above mentioned data mining task. We aim to investigate and develop various methodologies to discover association rules and classification models which can help predict rare tilt and traction failures in sequences using past events that are less critical. The investigated techniques constitute two major axes: Association analysis, which is temporal and Classification techniques, which is not temporal. The main challenges confronting the data mining task and increasing its complexity are mainly the rarity of the target events to be predicted in addition to the heavy redundancy of some events and the frequent occurrence of data bursts. The results obtained on real datasets collected from a fleet of trains allows to highlight the effectiveness of the approaches and methodologies used
Hussin, Burairah. "Development of a state prediction model to aid decision making in condition based maintenance". Thesis, University of Salford, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490430.
Pełny tekst źródłaKsiążki na temat "MAINTENANCE PREDICTION"
Foundation, AWWA Research, i American Water Works Association, red. Main break prediction, prevention, and control. Denver, Colo: Awwa Research Foundation, 2007.
Znajdź pełny tekst źródłaTaynor, Janet. Prediction model for estimating performance impacts of maintenance stress. Brooks Air Force Base, Tex: Air Force Systems Command, Air Force Human Resources Laboratory, 1988.
Znajdź pełny tekst źródłaHu, Changhua, Hongdong Fan i Zhaoqiang Wang. Residual Life Prediction and Optimal Maintenance Decision for a Piece of Equipment. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-2267-0.
Pełny tekst źródłaLiebermann, R. C. Stony Brook seismic network on Long Island, New York: Operation and maintenance, final report September 1979 - March 1985. Washington, D.C: Division of Radiation Programs and Earth Sciences, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1986.
Znajdź pełny tekst źródłaGregory, Williamson, Weyers Richard E, Brown Michael Carey 1969-, Sprinkel Michael M, Virginia Transportation Research Council i Virginia. Dept. of Transportation., red. Bridge deck service life prediction and costs. Charlottesville, Va: Virginia Transportation Research Council, 2007.
Znajdź pełny tekst źródłaInternational RILEM Workshop on Life Prediction and Aging Management of Concrete Structures (2003 Paris, France). 2nd International RILEM Workshop on Life Prediction and Aging Management of Concrete Structures : Paris, France, 5-6 May 2003. Bagneux: RILEM Publications, 2003.
Znajdź pełny tekst źródłaBartels, Bjoern. Strategies to the prediction, mitigation and management of product obsolescence. Hoboken, NJ: Wiley, 2012.
Znajdź pełny tekst źródłaYouakim, Samer Amir. A simplified method for prediction of long-term prestress loss in post-tensioned concrete bridges. La Jolla, Calif: Dept. of Structural Engineering, University of California, San Diego, 2006.
Znajdź pełny tekst źródłaPecht, Michael. Life-cycle forecasting, mitigation assessment, and obsolescence strategies: A guide to the prediction and management of electronic parts obsolescence. College Park, Md: CALCE EPSC Press, 2002.
Znajdź pełny tekst źródłaAn introduction to predictive maintenance. New York, NY: Van Nostrand Reinhold, 1990.
Znajdź pełny tekst źródłaCzęści książek na temat "MAINTENANCE PREDICTION"
Torim, Ants, Innar Liiv, Chahinez Ounoughi i Sadok Ben Yahia. "Pattern Based Software Architecture for Predictive Maintenance". W 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.
Pełny tekst źródłaPohlkötter, Fabian J., Dominik Straubinger, Alexander M. Kuhn, Christian Imgrund i William Tekouo. "Unlocking the Potential of Digital Twins". W Advances in Automotive Production Technology – Towards Software-Defined Manufacturing and Resilient Supply Chains, 190–99. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27933-1_18.
Pełny tekst źródłaOrchard, Marcos E., i David E. Acuña. "On Prognostic Algorithm Design and Fundamental Precision Limits in Long-Term Prediction". W Predictive Maintenance in Dynamic Systems, 355–79. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_12.
Pełny tekst źródłaGómez Fernández, Juan Francisco, Jesús Ferrero Bermejo, Fernando Agustín Olivencia Polo, Adolfo Crespo Márquez i Gonzalo Cerruela García. "Dynamic Reliability Prediction of Asset Failure Modes". W Advanced Maintenance Modelling for Asset Management, 291–309. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58045-6_12.
Pełny tekst źródłaLughofer, Edwin, Alexandru-Ciprian Zavoianu, Mahardhika Pratama i Thomas Radauer. "Automated Process Optimization in Manufacturing Systems Based on Static and Dynamic Prediction Models". W Predictive Maintenance in Dynamic Systems, 485–531. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_17.
Pełny tekst źródłaWu, Peggy, Jacquelyn Morie, J. Benton, Kip Haynes, Eric Chance, Tammy Ott i Sonja Schmer-Galunder. "Social Maintenance and Psychological Support Using Virtual Worlds". W Social Computing, Behavioral-Cultural Modeling and Prediction, 393–402. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05579-4_48.
Pełny tekst źródłaAnderson, Ronald T., i Lewis Neri. "The Army Aircraft Flight Safety Prediction Model". W Reliability-Centered Maintenance: Management and Engineering Methods, 275–311. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0757-7_6.
Pełny tekst źródłaDe Lucia, Andrea, Eugenio Pompella i Silvio Stefanucci. "Assessing Effort Prediction Models for Corrective Software Maintenance". W Enterprise Information Systems VI, 55–62. Dordrecht: Springer Netherlands, 2006. http://dx.doi.org/10.1007/1-4020-3675-2_7.
Pełny tekst źródłaBharathi, V., i Udaya Shastry. "Neural Network Based Effort Prediction Model for Maintenance Projects". W Communications in Computer and Information Science, 236–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21233-8_29.
Pełny tekst źródłaZeng, Yi, Wei Jiang, Changan Zhu, Jianfeng Liu, Weibing Teng i Yidong Zhang. "Prediction of Equipment Maintenance Using Optimized Support Vector Machine". W Lecture Notes in Computer Science, 570–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-37275-2_69.
Pełny tekst źródłaStreszczenia konferencji na temat "MAINTENANCE PREDICTION"
Mishra, KamalaKanta, i Sachin Kumar Manjhi. "Failure Prediction Model for Predictive Maintenance". W 2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM). IEEE, 2018. http://dx.doi.org/10.1109/ccem.2018.00019.
Pełny tekst źródłaZhou, J., X. Li, A. J. R. Andernroomer, H. Zeng, K. M. Goh, Y. S. Wong i G. S. Hong. "Intelligent prediction monitoring system for predictive maintenance in manufacturing". W 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005. IEEE, 2005. http://dx.doi.org/10.1109/iecon.2005.1569264.
Pełny tekst źródłaHafeez, Abdul Basit, Eduardo Alonso i Aram Ter-Sarkisov. "Towards Sequential Multivariate Fault Prediction for Vehicular Predictive Maintenance". W 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2021. http://dx.doi.org/10.1109/icmla52953.2021.00167.
Pełny tekst źródłaBundasak, Supaporn, i Pawin Wittayasirikul. "Predictive maintenance using AI for Motor health prediction system". W 2022 International Electrical Engineering Congress (iEECON). IEEE, 2022. http://dx.doi.org/10.1109/ieecon53204.2022.9741620.
Pełny tekst źródłaSu, Xiaobo, Qi Gao, Qingchun Wu i Jingxiong Gao. "Preventive Maintenance Task Prediction Based on Hierarchical Maintenance Conversion Law". W 2020 Prognostics and Health Management Conference (PHM-Besançon). IEEE, 2020. http://dx.doi.org/10.1109/phm-besancon49106.2020.00054.
Pełny tekst źródłaMosallam, Ahmed, Stefan Byttner, Magnus Svensson i Thorsteinn Rognvaldsson. "Nonlinear Relation Mining for Maintenance Prediction". W 2011 IEEE Aerospace Conference. IEEE, 2011. http://dx.doi.org/10.1109/aero.2011.5747581.
Pełny tekst źródłaKorvesis, Panagiotis, Stephane Besseau i Michalis Vazirgiannis. "Predictive Maintenance in Aviation: Failure Prediction from Post-Flight Reports". W 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00160.
Pełny tekst źródłaYuguo Xu, Yaohui Zhang i Shixin Zhang. "Uncertain generalized remaining useful life prediction-driven predictive maintenance decision". W 2015 Prognostics and System Health Management Conference (PHM). IEEE, 2015. http://dx.doi.org/10.1109/phm.2015.7380097.
Pełny tekst źródłaOlariu, Eliza Maria, Raluca Portase, Ramona Tolas i Rodica Potolea. "Predictive Maintenance - Exploring strategies for Remaining Useful Life (RUL) prediction". W 2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2022. http://dx.doi.org/10.1109/iccp56966.2022.10053988.
Pełny tekst źródłavan Driel, W. D., J. G. J. Beijer, J. W. Bikker, C. H. M. van Blokland, C. Ankomah i B. Jacobs. "Color maintenance prediction for LED-based products". W 2018 19th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE). IEEE, 2018. http://dx.doi.org/10.1109/eurosime.2018.8369875.
Pełny tekst źródłaRaporty organizacyjne na temat "MAINTENANCE PREDICTION"
Ritchie, R. J., J. C. Notestine, J. S. Schmidt, J. N. Irvin i C. P. Vaziri. Prediction of Scheduled and Preventative Maintenance Workload. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1985. http://dx.doi.org/10.21236/ada153761.
Pełny tekst źródłaBubenik, T. A., R. D. Fischer, G. R. Whitacre, D. J. Jones, J. F. Kiefner, M. Cola i W. A. Bruce. API-WCR Investigation and Prediction of Cooling Rates During Pipeline Maintenance Welding. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), grudzień 1991. http://dx.doi.org/10.55274/r0011852.
Pełny tekst źródłaLeis. L51866 Field Studies to Support SCC Life Prediction Model. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), styczeń 1997. http://dx.doi.org/10.55274/r0010357.
Pełny tekst źródłaKim, Changmo, Ghazan Khan, Brent Nguyen i Emily L. Hoang. Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis. Mineta Transportation Institute, grudzień 2020. http://dx.doi.org/10.31979/mti.2020.1806.
Pełny tekst źródłaCheng i Wang. L52025 Calibration of the PRCI Thermal Analysis Model for Hot Tap Welding. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), styczeń 2004. http://dx.doi.org/10.55274/r0010298.
Pełny tekst źródłaChurch, Joshua, LaKenya Walker i Amy Bednar. JAIC Predictive Maintenance Dashboard user manual. Engineer Research and Development Center (U.S.), wrzesień 2021. http://dx.doi.org/10.21079/11681/41823.
Pełny tekst źródłaBeen. L52121 Coating Deterioration as a Precursor for SCC. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), grudzień 2004. http://dx.doi.org/10.55274/r0011093.
Pełny tekst źródłaKlein, Gary A., Sallie E. Gordon, Mark Palmisano i Angelo Mirabella. Comparison-Based Predictions and Recommendations for Army Maintenance Training Devices. Fort Belvoir, VA: Defense Technical Information Center, marzec 1985. http://dx.doi.org/10.21236/ada170942.
Pełny tekst źródłaUnknown, Author. WINMOP-R03 Performance of Offshore Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), czerwiec 2003. http://dx.doi.org/10.55274/r0011744.
Pełny tekst źródłaFoster, Michelle. Vibration Analysis - Presented to the MMWG Predictive Maintenance User’s Group. Office of Scientific and Technical Information (OSTI), sierpień 2023. http://dx.doi.org/10.2172/1996132.
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