Literatura académica sobre el tema "MAINTENANCE PREDICTION"
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Artículos de revistas sobre el tema "MAINTENANCE PREDICTION"
Marshall, David F. "Language Maintenance and Revival". Annual Review of Applied Linguistics 14 (marzo de 1994): 20–33. http://dx.doi.org/10.1017/s0267190500002798.
Texto completoXu, Peng, Rengkui Liu, Quanxin Sun y 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.
Texto completoNansamba, Salmah y 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, n.º 6 (28 de diciembre de 2022): 52–70. http://dx.doi.org/10.14738/tmlai.106.13645.
Texto completoKang, Ziqiu, Cagatay Catal y Bedir Tekinerdogan. "Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks". Sensors 21, n.º 3 (30 de enero de 2021): 932. http://dx.doi.org/10.3390/s21030932.
Texto completoFitra Azyus, Adryan, Sastra Kusuma Wijaya y Mohd Naved. "Determining RUL Predictive Maintenance on Aircraft Engines Using GRU". Journal of Mechanical, Civil and Industrial Engineering 3, n.º 3 (11 de diciembre de 2022): 79–84. http://dx.doi.org/10.32996/jmcie.2022.3.3.10.
Texto completoD., Ganga y Ramachandran V. "Adaptive prediction model for effective electrical machine maintenance". Journal of Quality in Maintenance Engineering 26, n.º 1 (18 de abril de 2019): 166–80. http://dx.doi.org/10.1108/jqme-12-2017-0087.
Texto completoTong, Guoqiang, Xinbo Qian y 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 de abril de 2022): 1–17. http://dx.doi.org/10.1155/2022/8655669.
Texto completoRodrigues, Joao, Jose Torres Farinha y Antonio Marques Cardoso. "Predictive Maintenance Tools – A Global Survey". WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 16 (22 de enero de 2021): 96–109. http://dx.doi.org/10.37394/23203.2021.16.7.
Texto completoGibiec, Mariusz. "Prediction of Machines Health with Application of an Intelligent Approach – a Mining Machinery Case Study". Key Engineering Materials 293-294 (septiembre de 2005): 661–68. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.661.
Texto completoZhu, Ya Hong, Ji Ping Cao, Wen Xia Sun, Yang Tao Fan y Zhi Hui Zhao. "Demand Forecasting Model Based on Equipment Maintenance Resources in Virtual Warehousing". Applied Mechanics and Materials 556-562 (mayo de 2014): 5442–49. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5442.
Texto completoTesis sobre el tema "MAINTENANCE PREDICTION"
Morrison, David J. "Prediction of software maintenance costs". Thesis, Edinburgh Napier University, 2001. http://researchrepository.napier.ac.uk/Output/3601.
Texto completoIshihara, Yasuo. "Prediction of human error in rail car maintenance". Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10629.
Texto completoHartmann, 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.
Texto completoKumbala, 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.
Texto completoPodda, 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.
Texto completoTse, 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.
Texto completoWan, 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.
Texto completoKaidis, 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.
Texto completoSammouri, 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.
Texto completoIn 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.
Texto completoLibros sobre el tema "MAINTENANCE PREDICTION"
Foundation, AWWA Research y American Water Works Association, eds. Main break prediction, prevention, and control. Denver, Colo: Awwa Research Foundation, 2007.
Buscar texto completoTaynor, 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.
Buscar texto completoHu, Changhua, Hongdong Fan y 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.
Texto completoLiebermann, 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.
Buscar texto completoGregory, Williamson, Weyers Richard E, Brown Michael Carey 1969-, Sprinkel Michael M, Virginia Transportation Research Council y Virginia. Dept. of Transportation., eds. Bridge deck service life prediction and costs. Charlottesville, Va: Virginia Transportation Research Council, 2007.
Buscar texto completoInternational 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.
Buscar texto completoBartels, Bjoern. Strategies to the prediction, mitigation and management of product obsolescence. Hoboken, NJ: Wiley, 2012.
Buscar texto completoYouakim, 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.
Buscar texto completoPecht, 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.
Buscar texto completoAn introduction to predictive maintenance. New York, NY: Van Nostrand Reinhold, 1990.
Buscar texto completoCapítulos de libros sobre el tema "MAINTENANCE PREDICTION"
Torim, Ants, Innar Liiv, Chahinez Ounoughi y Sadok Ben Yahia. "Pattern Based Software Architecture for Predictive Maintenance". En 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.
Texto completoPohlkötter, Fabian J., Dominik Straubinger, Alexander M. Kuhn, Christian Imgrund y William Tekouo. "Unlocking the Potential of Digital Twins". En 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.
Texto completoOrchard, Marcos E. y David E. Acuña. "On Prognostic Algorithm Design and Fundamental Precision Limits in Long-Term Prediction". En Predictive Maintenance in Dynamic Systems, 355–79. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_12.
Texto completoGómez Fernández, Juan Francisco, Jesús Ferrero Bermejo, Fernando Agustín Olivencia Polo, Adolfo Crespo Márquez y Gonzalo Cerruela García. "Dynamic Reliability Prediction of Asset Failure Modes". En 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.
Texto completoLughofer, Edwin, Alexandru-Ciprian Zavoianu, Mahardhika Pratama y Thomas Radauer. "Automated Process Optimization in Manufacturing Systems Based on Static and Dynamic Prediction Models". En Predictive Maintenance in Dynamic Systems, 485–531. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_17.
Texto completoWu, Peggy, Jacquelyn Morie, J. Benton, Kip Haynes, Eric Chance, Tammy Ott y Sonja Schmer-Galunder. "Social Maintenance and Psychological Support Using Virtual Worlds". En 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.
Texto completoAnderson, Ronald T. y Lewis Neri. "The Army Aircraft Flight Safety Prediction Model". En 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.
Texto completoDe Lucia, Andrea, Eugenio Pompella y Silvio Stefanucci. "Assessing Effort Prediction Models for Corrective Software Maintenance". En Enterprise Information Systems VI, 55–62. Dordrecht: Springer Netherlands, 2006. http://dx.doi.org/10.1007/1-4020-3675-2_7.
Texto completoBharathi, V. y Udaya Shastry. "Neural Network Based Effort Prediction Model for Maintenance Projects". En 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.
Texto completoZeng, Yi, Wei Jiang, Changan Zhu, Jianfeng Liu, Weibing Teng y Yidong Zhang. "Prediction of Equipment Maintenance Using Optimized Support Vector Machine". En 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.
Texto completoActas de conferencias sobre el tema "MAINTENANCE PREDICTION"
Mishra, KamalaKanta y Sachin Kumar Manjhi. "Failure Prediction Model for Predictive Maintenance". En 2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM). IEEE, 2018. http://dx.doi.org/10.1109/ccem.2018.00019.
Texto completoZhou, J., X. Li, A. J. R. Andernroomer, H. Zeng, K. M. Goh, Y. S. Wong y G. S. Hong. "Intelligent prediction monitoring system for predictive maintenance in manufacturing". En 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005. IEEE, 2005. http://dx.doi.org/10.1109/iecon.2005.1569264.
Texto completoHafeez, Abdul Basit, Eduardo Alonso y Aram Ter-Sarkisov. "Towards Sequential Multivariate Fault Prediction for Vehicular Predictive Maintenance". En 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2021. http://dx.doi.org/10.1109/icmla52953.2021.00167.
Texto completoBundasak, Supaporn y Pawin Wittayasirikul. "Predictive maintenance using AI for Motor health prediction system". En 2022 International Electrical Engineering Congress (iEECON). IEEE, 2022. http://dx.doi.org/10.1109/ieecon53204.2022.9741620.
Texto completoSu, Xiaobo, Qi Gao, Qingchun Wu y Jingxiong Gao. "Preventive Maintenance Task Prediction Based on Hierarchical Maintenance Conversion Law". En 2020 Prognostics and Health Management Conference (PHM-Besançon). IEEE, 2020. http://dx.doi.org/10.1109/phm-besancon49106.2020.00054.
Texto completoMosallam, Ahmed, Stefan Byttner, Magnus Svensson y Thorsteinn Rognvaldsson. "Nonlinear Relation Mining for Maintenance Prediction". En 2011 IEEE Aerospace Conference. IEEE, 2011. http://dx.doi.org/10.1109/aero.2011.5747581.
Texto completoKorvesis, Panagiotis, Stephane Besseau y Michalis Vazirgiannis. "Predictive Maintenance in Aviation: Failure Prediction from Post-Flight Reports". En 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00160.
Texto completoYuguo Xu, Yaohui Zhang y Shixin Zhang. "Uncertain generalized remaining useful life prediction-driven predictive maintenance decision". En 2015 Prognostics and System Health Management Conference (PHM). IEEE, 2015. http://dx.doi.org/10.1109/phm.2015.7380097.
Texto completoOlariu, Eliza Maria, Raluca Portase, Ramona Tolas y Rodica Potolea. "Predictive Maintenance - Exploring strategies for Remaining Useful Life (RUL) prediction". En 2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2022. http://dx.doi.org/10.1109/iccp56966.2022.10053988.
Texto completovan Driel, W. D., J. G. J. Beijer, J. W. Bikker, C. H. M. van Blokland, C. Ankomah y B. Jacobs. "Color maintenance prediction for LED-based products". En 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.
Texto completoInformes sobre el tema "MAINTENANCE PREDICTION"
Ritchie, R. J., J. C. Notestine, J. S. Schmidt, J. N. Irvin y C. P. Vaziri. Prediction of Scheduled and Preventative Maintenance Workload. Fort Belvoir, VA: Defense Technical Information Center, enero de 1985. http://dx.doi.org/10.21236/ada153761.
Texto completoBubenik, T. A., R. D. Fischer, G. R. Whitacre, D. J. Jones, J. F. Kiefner, M. Cola y W. A. Bruce. API-WCR Investigation and Prediction of Cooling Rates During Pipeline Maintenance Welding. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), diciembre de 1991. http://dx.doi.org/10.55274/r0011852.
Texto completoLeis. L51866 Field Studies to Support SCC Life Prediction Model. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), enero de 1997. http://dx.doi.org/10.55274/r0010357.
Texto completoKim, Changmo, Ghazan Khan, Brent Nguyen y 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, diciembre de 2020. http://dx.doi.org/10.31979/mti.2020.1806.
Texto completoCheng y Wang. L52025 Calibration of the PRCI Thermal Analysis Model for Hot Tap Welding. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), enero de 2004. http://dx.doi.org/10.55274/r0010298.
Texto completoChurch, Joshua, LaKenya Walker y Amy Bednar. JAIC Predictive Maintenance Dashboard user manual. Engineer Research and Development Center (U.S.), septiembre de 2021. http://dx.doi.org/10.21079/11681/41823.
Texto completoBeen. L52121 Coating Deterioration as a Precursor for SCC. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), diciembre de 2004. http://dx.doi.org/10.55274/r0011093.
Texto completoKlein, Gary A., Sallie E. Gordon, Mark Palmisano y Angelo Mirabella. Comparison-Based Predictions and Recommendations for Army Maintenance Training Devices. Fort Belvoir, VA: Defense Technical Information Center, marzo de 1985. http://dx.doi.org/10.21236/ada170942.
Texto completoUnknown, Author. WINMOP-R03 Performance of Offshore Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), junio de 2003. http://dx.doi.org/10.55274/r0011744.
Texto completoFoster, Michelle. Vibration Analysis - Presented to the MMWG Predictive Maintenance User’s Group. Office of Scientific and Technical Information (OSTI), agosto de 2023. http://dx.doi.org/10.2172/1996132.
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