Artykuły w czasopismach na temat „MAINTENANCE PREDICTION”
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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 źródłaSong, Weixing, Jingjing Wu, Jianshe Kang i Jun Zhang. "Research on maintenance spare parts requirement prediction based on LSTM recurrent neural network". Open Physics 19, nr 1 (1.01.2021): 618–27. http://dx.doi.org/10.1515/phys-2021-0072.
Pełny tekst źródłaYang, Jia, Yongkui Sun, Yuan Cao i Xiaoxi Hu. "Predictive Maintenance for Switch Machine Based on Digital Twins". Information 12, nr 11 (22.11.2021): 485. http://dx.doi.org/10.3390/info12110485.
Pełny tekst źródłaSegovia-Muñoz, D., X. Serrano-Guerrero i A. Barragán-Escandon. "Predictive maintenance in LED street lighting controlled with telemanagement system to improve current fault detection procedures using software tools". Renewable Energy and Power Quality Journal 20 (wrzesień 2022): 379–86. http://dx.doi.org/10.24084/repqj20.318.
Pełny tekst źródłaNordal, Helge, i Idriss El-Thalji. "Lifetime Benefit Analysis of Intelligent Maintenance: Simulation Modeling Approach and Industrial Case Study". Applied Sciences 11, nr 8 (13.04.2021): 3487. http://dx.doi.org/10.3390/app11083487.
Pełny tekst źródłaChen, Lei, Lijun Wei, Wenlong Li, Junhui Wang i Dongyang Han. "Fault Prediction of Mechanical Equipment Based on Hilbert–Full-Vector Spectrum and TCDAN". Applied Sciences 13, nr 8 (7.04.2023): 4655. http://dx.doi.org/10.3390/app13084655.
Pełny tekst źródłaMbembati, Hadija, Kwame Ibwe i Baraka Maiseli. "Maintenance Automation Architecture and Electrical Equipment Fault Prediction Method in Tanzania Secondary Distribution Networks". Tanzania Journal of Science 47, nr 3 (15.08.2021): 1138–53. http://dx.doi.org/10.4314/tjs.v47i3.23.
Pełny tekst źródłaOnawumi, A. S., A. Aremu, O. A. Ajiboso, O. O. Agboola, T. M. A. Olayanju i C. O. Osueke. "Development of strategic maintenance prediction model for critical equipment maintenance". Materials Today: Proceedings 44 (2021): 2820–27. http://dx.doi.org/10.1016/j.matpr.2020.12.1163.
Pełny tekst źródłaGómez-Pau, Álvaro, Jordi-Roger Riba i Manuel Moreno-Eguilaz. "Time Series RUL Estimation of Medium Voltage Connectors to Ease Predictive Maintenance Plans". Applied Sciences 10, nr 24 (17.12.2020): 9041. http://dx.doi.org/10.3390/app10249041.
Pełny tekst źródłaKarimpour, Mostafa, Lalith Hitihamillage, Najwa Elkhoury, Sara Moridpour i Reyhaneh Hesami. "Fuzzy Approach in Rail Track Degradation Prediction". Journal of Advanced Transportation 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/3096190.
Pełny tekst źródłaYang, Dezhen, Yidan Cui, Quan Xia, Fusheng Jiang, Yi Ren, Bo Sun, Qiang Feng, Zili Wang i Chao Yang. "A Digital Twin-Driven Life Prediction Method of Lithium-Ion Batteries Based on Adaptive Model Evolution". Materials 15, nr 9 (6.05.2022): 3331. http://dx.doi.org/10.3390/ma15093331.
Pełny tekst źródłaMIZUTANI, YOSHIKATSU. "Trial of warfarin maintenance dose prediction." Rinsho yakuri/Japanese Journal of Clinical Pharmacology and Therapeutics 26, nr 1 (1995): 177–78. http://dx.doi.org/10.3999/jscpt.26.177.
Pełny tekst źródłaLangeron, Yves, Mitra Fouladirad i Antoine Grall. "Controlled systems, failure prediction and maintenance". IFAC-PapersOnLine 49, nr 12 (2016): 805–8. http://dx.doi.org/10.1016/j.ifacol.2016.07.873.
Pełny tekst źródłaMaktoubian, Jamal, Mohammad Sadegh Taskhiri i Paul Turner. "Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities". Forests 12, nr 11 (29.10.2021): 1495. http://dx.doi.org/10.3390/f12111495.
Pełny tekst źródłaPeng, Cheng, Yufeng Chen, Qing Chen, Zhaohui Tang, Lingling Li i Weihua Gui. "A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion". Sensors 21, nr 2 (8.01.2021): 418. http://dx.doi.org/10.3390/s21020418.
Pełny tekst źródłaPeng, Cheng, Yufeng Chen, Qing Chen, Zhaohui Tang, Lingling Li i Weihua Gui. "A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion". Sensors 21, nr 2 (8.01.2021): 418. http://dx.doi.org/10.3390/s21020418.
Pełny tekst źródłaLiu, Li, Zhihui Zhang, Zhijian Qu i Adrian Bell. "Remaining Useful Life Prediction for a Catenary, Utilizing Bayesian Optimization of Stacking". Electronics 12, nr 7 (6.04.2023): 1744. http://dx.doi.org/10.3390/electronics12071744.
Pełny tekst źródłaAl-Refaie, Abbas, Banan Abu Hamdieh i Natalija Lepkova. "Prediction of Maintenance Activities Using Generalized Sequential Pattern and Association Rules in Data Mining". Buildings 13, nr 4 (3.04.2023): 946. http://dx.doi.org/10.3390/buildings13040946.
Pełny tekst źródłaTARIK, Mouna, Ayoub MNIAI i Khalid JEBARI. "HYBRID FEATURE SELECTION AND SUPPORT VECTOR MACHINE FRAMEWORK FOR PREDICTING MAINTENANCE FAILURES". Applied Computer Science 19, nr 2 (30.06.2023): 112–24. http://dx.doi.org/10.35784/acs-2023-18.
Pełny tekst źródłaAlmazaideh, Mohammed, i Janos Levendovszky. "A predictive maintenance system for wireless sensor networks: a machine learning approach". Indonesian Journal of Electrical Engineering and Computer Science 25, nr 2 (1.02.2022): 1047. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp1047-1058.
Pełny tekst źródłaCao, Qiushi, Ahmed Samet, Cecilia Zanni-Merk, François de Bertrand de Beuvron i Christoph Reich. "Combining chronicle mining and semantics for predictive maintenance in manufacturing processes". Semantic Web 11, nr 6 (29.10.2020): 927–48. http://dx.doi.org/10.3233/sw-200406.
Pełny tekst źródłaHosseini, Seyed Amirhossein, i Omar Smadi. "How Prediction Accuracy Can Affect the Decision-Making Process in Pavement Management System". Infrastructures 6, nr 2 (11.02.2021): 28. http://dx.doi.org/10.3390/infrastructures6020028.
Pełny tekst źródłade Solminihac, Hernán, Marcelo G. Bustos, Aníbal L. Altamira i Juan Pablo Covarrubias. "Functional distress modelling in Portland cement concrete pavements". Canadian Journal of Civil Engineering 30, nr 4 (1.08.2003): 696–703. http://dx.doi.org/10.1139/l03-016.
Pełny tekst źródłaKoprinkova-Hristova, P. "Reinforcement Learning for Predictive Maintenance of Industrial Plants". Information Technologies and Control 11, nr 1 (1.03.2013): 21–28. http://dx.doi.org/10.2478/itc-2013-0004.
Pełny tekst źródłaCustode, Leonardo Lucio, Hyunho Mo, Andrea Ferigo i Giovanni Iacca. "Evolutionary Optimization of Spiking Neural P Systems for Remaining Useful Life Prediction". Algorithms 15, nr 3 (19.03.2022): 98. http://dx.doi.org/10.3390/a15030098.
Pełny tekst źródłaAlsolai, Hadeel, i Marc Roper. "The Impact of Ensemble Techniques on Software Maintenance Change Prediction: An Empirical Study". Applied Sciences 12, nr 10 (22.05.2022): 5234. http://dx.doi.org/10.3390/app12105234.
Pełny tekst źródłaVelimirović, Lazar Z., Radmila Janković, Jelena D. Velimirović i Aleksandar Janjić. "Wastewater Plant Reliability Prediction Using the Machine Learning Classification Algorithms". Symmetry 13, nr 8 (18.08.2021): 1518. http://dx.doi.org/10.3390/sym13081518.
Pełny tekst źródłaBettig, Bernhard P., i Ray P. S. Han. "Predictive Maintenance Using the Rotordynamic Model of a Hydraulic Turbine-Generator Rotor". Journal of Vibration and Acoustics 120, nr 2 (1.04.1998): 441–48. http://dx.doi.org/10.1115/1.2893849.
Pełny tekst źródłaAbidi, Mustufa Haider, Muneer Khan Mohammed i Hisham Alkhalefah. "Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing". Sustainability 14, nr 6 (14.03.2022): 3387. http://dx.doi.org/10.3390/su14063387.
Pełny tekst źródłaWang, Youdao, i Yifan Zhao. "Multi-Scale Remaining Useful Life Prediction Using Long Short-Term Memory". Sustainability 14, nr 23 (24.11.2022): 15667. http://dx.doi.org/10.3390/su142315667.
Pełny tekst źródłaChui, Kwok Tai, Brij B. Gupta i Pandian Vasant. "A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine". Electronics 10, nr 3 (25.01.2021): 285. http://dx.doi.org/10.3390/electronics10030285.
Pełny tekst źródłaKaya, Ertuğrul, Daniele Farioli i Matteo Strano. "FEA Approach for Wear and Damage Prediction of Tools for the Progressive Die Stamping of Steel Washers". Key Engineering Materials 926 (22.07.2022): 1168–77. http://dx.doi.org/10.4028/p-15186x.
Pełny tekst źródłaSresakoolchai, Jessada, i Sakdirat Kaewunruen. "Track Geometry Prediction Using Three-Dimensional Recurrent Neural Network-Based Models Cross-Functionally Co-Simulated with BIM". Sensors 23, nr 1 (30.12.2022): 391. http://dx.doi.org/10.3390/s23010391.
Pełny tekst źródłaMarinelli, Marina, Sergios Lambropoulos i Kleopatra Petroutsatou. "Earthmoving trucks condition level prediction using neural networks". Journal of Quality in Maintenance Engineering 20, nr 2 (6.05.2014): 182–92. http://dx.doi.org/10.1108/jqme-09-2012-0031.
Pełny tekst źródłaFeng, Jianshe, Haoshu Cai, Zongchang Liu i Jay Lee. "A Systematic Framework for Maintenance Scheduling and Routing for Off-Shore Wind Farms by Minimizing Predictive Production Loss". E3S Web of Conferences 233 (2021): 01063. http://dx.doi.org/10.1051/e3sconf/202123301063.
Pełny tekst źródłaPaprocka, Iwona, Wojciech M. Kempa, Krzysztof Kalinowski i Cezary Grabowik. "A Production Scheduling Model with Maintenance". Advanced Materials Research 1036 (październik 2014): 885–90. http://dx.doi.org/10.4028/www.scientific.net/amr.1036.885.
Pełny tekst źródłaCaricato, A., A. Ficarella i L. Spada Chiodo. "Prognostic techniques for aeroengine health assessment and Remaining Useful Life estimation". E3S Web of Conferences 312 (2021): 11017. http://dx.doi.org/10.1051/e3sconf/202131211017.
Pełny tekst źródłaGopikuttan, Lasithan Lasyam, Shouri Puthan Veettil i Rajesh Vazhayil Govindan. "Maintenance Initiation Prediction Incorporating Vibrations and System Availability". Advances in Technology Innovation 7, nr 3 (11.03.2022): 181–94. http://dx.doi.org/10.46604/aiti.2022.8618.
Pełny tekst źródłaMilad, Abdalrhman Abrahim, Sayf A. Majeed i Nur Izzi Md Yusoff. "Comparative Study of Utilising Neural Network and Response Surface Methodology for Flexible Pavement Maintenance Treatments". Civil Engineering Journal 6, nr 10 (1.10.2020): 1895–905. http://dx.doi.org/10.28991/cej-2020-03091590.
Pełny tekst źródłaYeh, Chia-Hung, Min-Hui Lin, Chien-Hung Lin, Cheng-En Yu i Mei-Juan Chen. "Machine Learning for Long Cycle Maintenance Prediction of Wind Turbine". Sensors 19, nr 7 (8.04.2019): 1671. http://dx.doi.org/10.3390/s19071671.
Pełny tekst źródłaHORII, Masafumi, i Tadashi FUKUDA. "Pavement Ice Prediction System in Winter Maintenance." Doboku Gakkai Ronbunshu, nr 669 (2001): 243–51. http://dx.doi.org/10.2208/jscej.2001.669_243.
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