Статті в журналах з теми "Prognostics prediction model"
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Long, Bing, Xiangnan Li, Xiaoyu Gao, and Zhen Liu. "Prognostics Comparison of Lithium-Ion Battery Based on the Shallow and Deep Neural Networks Model." Energies 12, no. 17 (August 25, 2019): 3271. http://dx.doi.org/10.3390/en12173271.
Повний текст джерелаLi, Xiaochuan, Xiaoyu Yang, Yingjie Yang, Ian Bennett, and David Mba. "An intelligent diagnostic and prognostic framework for large-scale rotating machinery in the presence of scarce failure data." Structural Health Monitoring 19, no. 5 (October 29, 2019): 1375–90. http://dx.doi.org/10.1177/1475921719884019.
Повний текст джерелаTong, Guoqiang, Xinbo Qian, and Yilai Liu. "Prognostics and Predictive Maintenance Optimization Based on Combination BP-RBF-GRNN Neural Network Model and Proportional Hazard Model." Journal of Sensors 2022 (April 29, 2022): 1–17. http://dx.doi.org/10.1155/2022/8655669.
Повний текст джерелаWon, Dong-Yeon, Hyun Su Sim, and Yong Soo Kim. "Prediction of Remaining Useful Lifetime of Membrane Using Machine Learning." Science of Advanced Materials 12, no. 10 (October 1, 2020): 1485–91. http://dx.doi.org/10.1166/sam.2020.3788.
Повний текст джерелаWang, Yiwei, Christian Gogu, Nicolas Binaud, Christian Bes, Raphael T. Haftka, and Nam-Ho Kim. "Predictive airframe maintenance strategies using model-based prognostics." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 6 (March 1, 2018): 690–709. http://dx.doi.org/10.1177/1748006x18757084.
Повний текст джерелаWang, Xin, Yi Li, Yaxi Xu, Xiaodong Liu, Tao Zheng, and Bo Zheng. "Remaining Useful Life Prediction for Aero-Engines Using a Time-Enhanced Multi-Head Self-Attention Model." Aerospace 10, no. 1 (January 13, 2023): 80. http://dx.doi.org/10.3390/aerospace10010080.
Повний текст джерелаZhiyong, Gao, Li Jiwu, and Wang Rongxi. "Prognostics uncertainty reduction by right-time prediction of remaining useful life based on hidden Markov model and proportional hazard model." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 1 (January 2, 2021): 154–65. http://dx.doi.org/10.17531/ein.2021.1.16.
Повний текст джерелаChen, Xuefeng, Zhongjie Shen, Zhengjia He, Chuang Sun, and Zhiwen Liu. "Remaining life prognostics of rolling bearing based on relative features and multivariable support vector machine." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 227, no. 12 (January 11, 2013): 2849–60. http://dx.doi.org/10.1177/0954406212474395.
Повний текст джерелаXie, Zhiyuan, Shichang Du, Jun Lv, Yafei Deng, and Shiyao Jia. "A Hybrid Prognostics Deep Learning Model for Remaining Useful Life Prediction." Electronics 10, no. 1 (December 29, 2020): 39. http://dx.doi.org/10.3390/electronics10010039.
Повний текст джерелаMuneer, Amgad, Shakirah Mohd Taib, Sheraz Naseer, Rao Faizan Ali, and Izzatdin Abdul Aziz. "Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis." Electronics 10, no. 20 (October 9, 2021): 2453. http://dx.doi.org/10.3390/electronics10202453.
Повний текст джерелаXie, Mingjiang, Zishuo Li, Jianli Zhao, and Xianjun Pei. "A Prognostics Method Based on Back Propagation Neural Network for Corroded Pipelines." Micromachines 12, no. 12 (December 16, 2021): 1568. http://dx.doi.org/10.3390/mi12121568.
Повний текст джерелаCheng, Shan-Jen, Wen-Ken Li, Te-Jen Chang, and Chang-Hung Hsu. "Data-Driven Prognostics of the SOFC System Based on Dynamic Neural Network Models." Energies 14, no. 18 (September 15, 2021): 5841. http://dx.doi.org/10.3390/en14185841.
Повний текст джерелаLiu, Yumei, Ningguo Qiao, Congcong Zhao, Jiaojiao Zhuang, and Guangdong Tian. "Using the AR–SVR–CPSO hybrid model to forecast vibration signals in a high-speed train transmission system." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 233, no. 7 (October 18, 2018): 701–14. http://dx.doi.org/10.1177/0954409718804908.
Повний текст джерелаKhan, Faisal, Omer Eker, Atif Khan, and Wasim Orfali. "Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine." Data 3, no. 4 (November 6, 2018): 49. http://dx.doi.org/10.3390/data3040049.
Повний текст джерелаSu, Xiaohong, Shuai Wang, Michael Pecht, Peijun Ma, and Lingling Zhao. "Prognostics of lithium-ion batteries based on different dimensional state equations in the particle filtering method." Transactions of the Institute of Measurement and Control 39, no. 10 (April 22, 2016): 1537–46. http://dx.doi.org/10.1177/0142331216642836.
Повний текст джерелаVernez, Simone Lucia, Yair Lotan, Shahrokh F. Shariat, Arthur I. Sagalowsky, Jacob B. Morgan, Jay D. Raman, Christopher G. Wood, et al. "Predictive models for improved prognostication and selection of neoadjuvant and adjuvant systemic chemotherapy in upper tract urothelial cell carcinoma." Journal of Clinical Oncology 34, no. 2_suppl (January 10, 2016): 456. http://dx.doi.org/10.1200/jco.2016.34.2_suppl.456.
Повний текст джерелаBoutrous, Khoury, Iury Bessa, Vicenç Puig, Fatiha Nejjari, and Reinaldo M. Palhares. "Data-driven Prognostics based on Evolving Fuzzy Degradation Models for Power Semiconductor Devices." PHM Society European Conference 7, no. 1 (June 29, 2022): 68–77. http://dx.doi.org/10.36001/phme.2022.v7i1.3338.
Повний текст джерелаZhu, Hongmin. "Real-Time Prognostics of Engineered Systems under Time Varying External Conditions Based on the COX PHM and VARX Hybrid Approach." Sensors 21, no. 5 (March 2, 2021): 1712. http://dx.doi.org/10.3390/s21051712.
Повний текст джерелаLi, Hongru, Zaike Tian, He Yu, and Baohua Xu. "Fault Prognosis of Hydraulic Pump Based on Bispectrum Entropy and Deep Belief Network." Measurement Science Review 19, no. 5 (October 1, 2019): 195–203. http://dx.doi.org/10.2478/msr-2019-0025.
Повний текст джерелаKhumprom, Phattara, David Grewell, and Nita Yodo. "Deep Neural Network Feature Selection Approaches for Data-Driven Prognostic Model of Aircraft Engines." Aerospace 7, no. 9 (September 4, 2020): 132. http://dx.doi.org/10.3390/aerospace7090132.
Повний текст джерелаVerstraete, David, Enrique Droguett, and Mohammad Modarres. "A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics." Sensors 20, no. 1 (December 27, 2019): 176. http://dx.doi.org/10.3390/s20010176.
Повний текст джерелаWang, Jiwei, Zhongwei Deng, Kaile Peng, Xinchen Deng, Lijun Xu, Guoqing Guan, and Abuliti Abudula. "Early Prognostics of Lithium-Ion Battery Pack Health." Sustainability 14, no. 4 (February 17, 2022): 2313. http://dx.doi.org/10.3390/su14042313.
Повний текст джерелаDong, Jiankang, Jiaqi Wang, and Runxia Guo. "Remaining useful life prognostics for the electro-hydraulic actuator using relevance vector machine and optimized on-line incremental learning." MATEC Web of Conferences 277 (2019): 02009. http://dx.doi.org/10.1051/matecconf/201927702009.
Повний текст джерелаDing, Gang, Da Lei, and Wei Yao. "Health Condition Prognostics of Complex Equipment Based on Discrete Input Process Neural Networks." Applied Mechanics and Materials 423-426 (September 2013): 2347–54. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2347.
Повний текст джерелаBernardino, Lucas Ferreira, André Felipe Ferreira de Souza, Argimiro Resende Secchi, Maurício Bezerra de Souza Jr., and Anne Barros. "Integration of Prognostics and Control of an Oil/CO2 Subsea Separation System." Processes 8, no. 2 (January 23, 2020): 148. http://dx.doi.org/10.3390/pr8020148.
Повний текст джерелаLi, Mingfei, Jiajian Wu, Zhengpeng Chen, Jiangbo Dong, Zhiping Peng, Kai Xiong, Mumin Rao, Chuangting Chen, and Xi Li. "Data-Driven Voltage Prognostic for Solid Oxide Fuel Cell System Based on Deep Learning." Energies 15, no. 17 (August 29, 2022): 6294. http://dx.doi.org/10.3390/en15176294.
Повний текст джерелаTseng, Fling, Dimitar Filev, Murat Yildirim, and Ratna Babu Chinnam. "Online System Prognostics with Ensemble Models and Evolving Clustering." Machines 11, no. 1 (December 29, 2022): 40. http://dx.doi.org/10.3390/machines11010040.
Повний текст джерелаBlair, Jennifer, Bruce Stephen, Blair Brown, Alistair Forbes, and Stephen Mcarthur. "Hybrid Fault Prognostics for Nuclear Applications: Addressing Rotating Plant Model Uncertainty." PHM Society European Conference 7, no. 1 (June 29, 2022): 58–67. http://dx.doi.org/10.36001/phme.2022.v7i1.3321.
Повний текст джерелаQin, Aisong, Qinghua Zhang, Qin Hu, Guoxi Sun, Jun He, and Shuiquan Lin. "Remaining Useful Life Prediction for Rotating Machinery Based on Optimal Degradation Indicator." Shock and Vibration 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/6754968.
Повний текст джерелаDong, Han Cheng, San Tong Zhang, Qing Hua Li, and Chang Hong Wang. "A New Approach to Battery Capacity Prediction Based on Hybrid ARMA and ANN Model." Applied Mechanics and Materials 190-191 (July 2012): 241–44. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.241.
Повний текст джерелаKhumprom, Phattara, and Nita Yodo. "A Data-Driven Predictive Prognostic Model for Lithium-Ion Batteries based on a Deep Learning Algorithm." Energies 12, no. 4 (February 18, 2019): 660. http://dx.doi.org/10.3390/en12040660.
Повний текст джерелаTsoutsanis, Elias, Yi-Guang Li, Pericles Pilidis, and Mike Newby. "Non-linear model calibration for off-design performance prediction of gas turbines with experimental data." Aeronautical Journal 121, no. 1245 (September 18, 2017): 1758–77. http://dx.doi.org/10.1017/aer.2017.96.
Повний текст джерелаFigueroa Barraza, Joaquín, Enrique López Droguett, and Marcelo Ramos Martins. "Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks." Sensors 21, no. 17 (September 1, 2021): 5888. http://dx.doi.org/10.3390/s21175888.
Повний текст джерелаLi, Ni, and Zhen Hua Li. "The Fault Prediction of Aerospace Equipment PHM Technology and its Demonstrated Failure Prediction Module Simulation." Advanced Materials Research 505 (April 2012): 239–44. http://dx.doi.org/10.4028/www.scientific.net/amr.505.239.
Повний текст джерелаMaulana, Faisal, Andrew Starr, and Agusmian Partogi Ompusunggu. "Explainable Data-Driven Method Combined with Bayesian Filtering for Remaining Useful Lifetime Prediction of Aircraft Engines Using NASA CMAPSS Datasets." Machines 11, no. 2 (January 24, 2023): 163. http://dx.doi.org/10.3390/machines11020163.
Повний текст джерелаLaddada, S., T. Benkedjouh, M. O. Si- Chaib, and R. DRAI. "Remaining useful life prediction of cutting tools using wavelet packet transform and extreme learning machine." Algerian Journal of Signals and Systems 3, no. 4 (December 15, 2018): 156–65. http://dx.doi.org/10.51485/ajss.v3i4.72.
Повний текст джерелаHansen, R. J., D. L. Hall, and S. K. Kurtz. "A New Approach to the Challenge of Machinery Prognostics." Journal of Engineering for Gas Turbines and Power 117, no. 2 (April 1, 1995): 320–25. http://dx.doi.org/10.1115/1.2814097.
Повний текст джерелаLiu, Chenyu, Alexandre Mauricio, Junyu Qi, Dandan Peng, and Konstantinos Gryllias. "Domain Adaptation Digital Twin for Rolling Element Bearing Prognostics." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 10. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1294.
Повний текст джерелаGhavami, Peter, and Kailash Kapur. "Prognostics and Prediction of Patient Health Status Using a Multi-Model Artificial Intelligence Framework." Public Health Frontier 2, no. 2 (June 26, 2013): 46–60. http://dx.doi.org/10.5963/phf0202001.
Повний текст джерелаHagmeyer, Simon, Peter Zeiler, and Marco F. Huber. "On the Integration of Fundamental Knowledge about Degradation Processes into Data-Driven Diagnostics and Prognostics Using Theory-Guided Data Science." PHM Society European Conference 7, no. 1 (June 29, 2022): 156–65. http://dx.doi.org/10.36001/phme.2022.v7i1.3352.
Повний текст джерелаKong, Zhengmin, Yande Cui, Zhou Xia, and He Lv. "Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics." Applied Sciences 9, no. 19 (October 3, 2019): 4156. http://dx.doi.org/10.3390/app9194156.
Повний текст джерелаZhao, Yuntian, Maxwell Toothman, James Moyne, and Kira Barton. "An Adaptive Modeling Framework for Bearing Failure Prediction." Electronics 11, no. 2 (January 14, 2022): 257. http://dx.doi.org/10.3390/electronics11020257.
Повний текст джерелаWang, Yi, Ming Qing Xiao, and Jia Yong Fang. "Integrate Uncertainty in the Process of Prognostics for Electronics." Applied Mechanics and Materials 69 (July 2011): 132–37. http://dx.doi.org/10.4028/www.scientific.net/amm.69.132.
Повний текст джерелаDing, Fangfang, and Zhigang Tian. "Integrated Prognosis for Wind Turbine Gearbox Condition-Based Maintenance Considering Time-Varying Load and Crack Initiation Time Uncertainty." International Journal of Reliability, Quality and Safety Engineering 28, no. 04 (February 23, 2021): 2150024. http://dx.doi.org/10.1142/s0218539321500248.
Повний текст джерелаZhou, Jianmin, Sen Gao, Jiahui Li, and Wenhao Xiong. "Bearing Life Prediction Method Based on Parallel Multichannel Recurrent Convolutional Neural Network." Shock and Vibration 2021 (October 13, 2021): 1–9. http://dx.doi.org/10.1155/2021/6142975.
Повний текст джерелаShoriat Ullah, MD, and Kangwon Seo. "Prediction of Lithium-Ion Battery Capacity by Functional Principal Component Analysis of Monitoring Data." Applied Sciences 12, no. 9 (April 24, 2022): 4296. http://dx.doi.org/10.3390/app12094296.
Повний текст джерелаHuang, Run Qing, Li Feng Xi, C. Richard Liu, and Jay Lee. "Prognostics for Ball Bearing Based on Neural Networks and Morlet Wavelet." Materials Science Forum 505-507 (January 2006): 1153–58. http://dx.doi.org/10.4028/www.scientific.net/msf.505-507.1153.
Повний текст джерелаTyagi, Ankita, and Ritika Mehra. "An optimized CNN based intelligent prognostics model for disease prediction and classification from Dermoscopy images." Multimedia Tools and Applications 79, no. 35-36 (July 18, 2020): 26817–35. http://dx.doi.org/10.1007/s11042-020-09074-3.
Повний текст джерелаChoi, Bokyung, Kenneth Santo-Domingo, Alan S. Penzias, Ernesto Bosch, Arthur Leader, Antonio Pellicer, and Mylene Yao. "Turning Past IVF Data into Personalized Prognostics through a Validated, Multi-Center IVF Prediction Model." Fertility and Sterility 99, no. 3 (March 2013): S10—S11. http://dx.doi.org/10.1016/j.fertnstert.2013.01.021.
Повний текст джерелаSun, Junjie, Lehui Zheng, Ying Huang, and Yu Ge. "Remaining Useful Life Prediction Based on CNN-BGRU-SA." Journal of Physics: Conference Series 2405, no. 1 (December 1, 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2405/1/012007.
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