An overview of the application of machine learning in predictive maintenance
Abstract
With the rise of industrial artificial intelligence (AI), smart sensing, and the Internet of Things (IoT), companies are learning how to use their data not only for analysing the past but also for predicting the future. Maintenance is a crucial area that can drive significant cost savings and production value around the world.
Predictive maintenance (PdM) is a technique that collects, cleans, analyses, and utilises data from various manufacturing and sensing sources like machines usage, operating conditions, and equipment feedback. It applies advanced algorithms to the data, automatically compares the fed data and the information from previous cases to anticipate or predict equipment failure before it happens, thus helping optimise equipment utilisation and maintenance strategies, improve performance and productivity, and extend equipment life. Robust PdM tools enable organisations to leverage and maximise the value of their existing data to stay ahead of potential breakdowns or disruptions in services, and address them proactively instead of reacting to issues as they arise. Therefore, it has attracted more and more attention of specialists in recent years.
This paper provides a comprehensive review of the recent advancements of machine learning (ML) techniques widely applied to PdM by classifying the research according to the ML algorithms, machinery and equipment used in data acquisition. Important contributions of the researchers are highlighted, leading to some guidelines and foundation for further studies. Currently, BIENDONG POC is running some pilot PdM projects for critical equipment in Hai Thach - Moc Tinh gas processing plant.
References
BS EN 1336:2017, "Maintenance - Maintenance terminology", 2017.
Raghu Raj, “How preventive maintenance can backfire and harm your assets”, 2020. [Online]. Available: https://www.ey.com/en_gl/consulting/how-preventive-maintenance-can-backfire-and-harm-your-assets.
Tomasz Nowakowski, Agnieszka Tubis, and Sylwia Werbińska-Wojciechowska, "Evolution of technical systems maintenance approaches-review and a case study", International Conference on Intelligent Systems in Production Engineering and Maintenance. Springer, 2018, pp. 161 - 174.
Tran Vu Tung, Tran Ngoc Trung, Ngo Huu Hai, and Nguyen Thanh Tinh, "Digital transformation in oil and gas companies - A case study of Bien Dong POC", Petrovietnam Journal, Vol. 10, pp. 67 - 78, 2020. DOI: 10.47800/PVJ.2020.10-07.
Trần Ngọc Trung, Trần Vũ Tùng, Hoàng Kỳ Sơn, Ngô Hữu Hải và Đào Quang Khoa, "Thực tiễn triển khai nền tảng số hóa tập trung tại mỏ Hải Thạch - Mộc Tinh”, Tạp chí Dầu khí, Số 12, trang 47 - 56, 2020. DOI: 10.47800/PVJ.2020.12-06.
Rebecca Smith-Bindman, Philip Chu, Diana Lynn Miglioretti, Chris Quale, Robert Daniel Rosenberg, Gary Cutter, Berta Geller, Peter Bacchetti, Edward A. Sickles, and Karla Kerlikowske, "Physician predictors of mammographic accuracy", Journal of the National Cancer Institute, Vol. 97, No. 5, pp. 358 - 367, 2005. DOI: 10.1093/jnci/dji060.
Varun Gulshan, Lily Peng, Marc Coram, Martin C. Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, Ramasamy Kim, Rajiv Raman, Philip C. Nelson, Jessica L. Mega, and Dale R. Webster, "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs", JAMA, Vol. 316, No. 22, pp. 2402 - 2410, 2016. DOI: 10.1001/jama.2016.17216.
Bruce I. Reiner and Elizabeth Krupinski, "The insidious problem of fatigue in medical imaging practice", Journal of Digital Imaging, Vol. 25, No. 1, pp. 3 - 6, 2012. DOI: 10.1007/s10278-011-9436-4.
Won Shin, Jeongyun Han, and Wonjong Rhee, "AI-assistance for predictive maintenance of renewable energy systems", Energy, Vol. 221, 2021. DOI: 10.1016/j.energy.2021.119775.
Roosefert Mohan T., Preetha Roselyn J., Annie Uthra R., Devaraj D., and Umachandran K., “Intelligent machine learning based total productive maintenance approach for achieving zero downtime in industrial machinery”, Computer & Industrial Engineering, Vol. 157, 2021.
M.Waqar Akramad, Guiqiang Li, Yi Jin, Xiao Chen, Changan Zhu, Xudong Zhao, Abdul Khaliq, M.Faheem, and Ashfaq Ahmad, "CNN based automatic detection of photovoltaic cell defects in electroluminescence images", Energy, Vol. 189, 2019. DOI: 10.1016/j.energy.2019.116319.
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun, "Faster R-CNN: Towards real-time object detection with region proposal networks", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, pp. 1137-1149, 2017. DOI: 10.1109/TPAMI.2016.2577031.
Young-Jin Cha, Wooram Choi, Gahyun Suh, Sadegh Mahmoudkhani, and Oral Büyüköztürk, "Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types", Computer-Aided Civil and Infrastructure Engineering, Vol. 33, No. 9, pp. 731 - 747, 2018. DOI: doi/10.1111/mice.12334.
Wenbin Wang, Matthew J. Carr, Tommy Wai Shing Chow, and Michael G. Pecht, "A two-level inspection model with technological insertions", IEEE Transactions on Reliability, Vol. 61, pp. 479 - 490, 2012. DOI: 10.1109/TR.2012.2183911.
Sule Selcuk, "Predictive maintenance, its implementation and latest trends", Journal of Engineering Manufacture, Vol. 231, No. 9, pp. 1670 - 1679, 2017. DOI: 10.1177/0954405415601640.
R. Keith Mobley, Lindley R. Higgins, and Darrin J. Wikoff, Maintenance engineering handbook, 7th edition. McGraw-Hill Education, 2008.
PwC, “Predictive maintenance 4.0 - Beyond the hype: PdM 4.0 delivers results”, 2018. [Online]. Available: https://www.pwc.de/de/industrielle-produktion/pwc-predictive-maintenance-4-0.pdf.
Valerio Dilda, Lapo Mori, Olivier Noterdaeme, and Christoph Schmitz, “Manufacturing: Analytics unleashes productivity and profitability”, 2017. [Online]. Available: https://www.mckinsey.com/business-functions/operations/our-insights/manufacturing-analytics-unleashes-productivity-and-profitability.
Knud Lasse Lueth, “Predictive maintenance initiatives saved organizations $17B in 2018, as the number of vendors surges”, 2019. [Online]. Available: https://iot-analytics.com/numbers-of-predictive-maintenance-vendors-surges/.
Yaguo Lei, Naipeng Li, Liang Guo, Ningbo Li, Tao Yan, and Jing Lin, "Machinery health prognostics: A systematic review from data acquisition to RUL prediction", Mechanical Systems and Signal Processing, Vol. 104, pp. 799 - 834, 2018. DOI: 10.1016/j.ymssp.2017.11.016.
Rui Zhao, Ruqiang Yan, Jinjiang Wang, and Kezhi Mao, "Learning to monitor machine health with convolutional bi-directional LSTM networks", Sensors, Vol. 17, No. 2, pp. 273, 2017. DOI: 10.3390/s17020273.
Ling Xiang, Penghe Wang, Xin Yang, Aijun Hu, and Hao Su, "Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism", Measurement, Vol. 175, No. 8, 2021. DOI: 10.1016/j.measurement.2021.109094.
Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, and Puneet Agarwal, “Long short term memory networks for anomaly detection in time series”, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, 2015.
Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff , "LSTM-based encoder-decoder for multi-sensor anomaly detection", 2016. [Online]. Available: https://arxiv.org/pdf/1607.00148.pdf.
Achmad Widodo and Bo-Suk Yang, "Application of relevance vector machine and survival probability to machine degradation assessment", Expert Systems with Applications, Vol. 38, No. 3, pp. 2592 - 2599, 2011. DOI: 10.1016/j.eswa.2010.08.049.
Tarak Benkedjouh, Kamal Medjaher, Noureddine Zerhouni, and Said Rechak, "Health assessment and life prediction of cutting tools based on support vector regression", Journal of Intelligent Manufacturing, Vol. 26, No. 2, pp. 213 - 223, 2015. DOI: 10.1007/s10845-013-0774-6.
Tianyi Wang, "Bearing life prediction based on vibration signals: A case study and lessons learned", IEEE Conference on Prognostics and Health Management, Beijing, 2012. DOI: 10.1109/ICPHM.2012.6299547.
Yuanhong Chang, Jinglong Chen, Haixin Lv, and Shen Liu, "Heterogeneous bi-directional recurrent neural network combining fusion health indicator for predictive analytics of rotating machinery", ISA Transactions, 2021. DOI: 10.1016/j.isatra.2021.04.024.
Khanh T. P. Nguyen and Kamal Medjaher, "An automated health indicator construction methodology for prognostics based on multi-criteria optimization", ISA Transactions, Vol. 113, pp. 81 - 96, 2021. DOI: 10.1016/j.isatra.2020.03.017.
Yaguo Lei, Naipeng Li, Liang Guo, Ningbo Li, Tao Yan, and Jing Lin, "Machinery health prognostics: A systematic review from data acquisition to RUL prediction", Mechanical Systems and Signal Processing, Vol. 104, pp. 799 - 834, 2018. DOI: 10.1016/j.ymssp.2017.11.016.
Jay Lee, Hai Qiu, Jing Lin, and Gang Yu, "Robust performance degradation assessment methods for enhanced rolling element bearing prognostics", Advanced Engineering Informatics, Vol. 17, No. 3 - 4, pp. 127 - 140, 2003. DOI: 10.1016/j.aei.2004.08.001.
Zheng Zhou, Sheng Hong, Enrico Zio, and Kan Hong, "Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method", Digital Signal Processing, Vol. 27, pp. 159 - 166, 2014. DOI: 10.1016/j.dsp.2013.12.010.
Yaguo Lei, Naipeng Li, Szymon Gontarz, Jing Lin, Stanislaw Radkowski, and Jacek Dybala, "A model-based method for remaining useful life prediction of machinery", IEEE Transactions on Reliability, Vol. 65, No. 3, pp. 1314 - 1326, 2016. DOI: 10.1109/TR.2016.2570568.
Linxia Liao, Radu Pavel, and Wenjing Jin, "Enhanced restricted Boltzmann machine with prognosability regularization for prognostics and health assessment", IEEE Transactions on Industrial, Vol. 63, No. 11, pp. 7076 - 7083, 2016. DOI: 10.1109/TIE.2016.2586442.
Runqing Huang, Lifeng Xi, Xinglin Li, C. Richard Liu, Hai Qiu, and Jay Lee, "Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods", Mechanical Systems and Signal Processing, Vol. 21, No. 1, pp. 193 - 207, 2007. DOI: 10.1016/j.ymssp.2005.11.008.
Yu Wang, Yizhen Peng, Zi, Yanyang Zi, Xiaohang Jin, and Kwok-Leung Tsui, "A two-stage data-driven-based prognostic approach for bearing degradation problem", IEEE Transactions on Industrial Informatics, Vol. 12, No. 3, pp. 924 - 932, 2016. DOI: 10.1109/TII.2016.2535368.
Xiaohang Jin, Yi Sun, Zijun Que, Yu Wang, and Tommy Wai Shing Chow, "Anomaly detection and fault prognosis for bearings", IEEE Transactions on Instrumentation and Measurement, Vol. 65, pp. 2046 - 2054, 2016. DOI: 10.1109/TIM.2016.2570398.
Kumar H S, P. Srinivasa Pai, N. S. Sriram, and Vijay Gs, "Rolling element bearing fault diagnostics: Development of health index", Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 1989 - 1996, Vol. 231, No. 21, pp. 3923 - 3939, 2017. DOI: 10.1177/0954406216656214.
Hasan Ocak, Kenneth A. Loparo, Fred M. Discenzo, "Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: A method for bearing prognostics", Journal of Sound and Vibration, Vol. 302, No. 4 - 5, pp. 951 - 961, 2007. DOI: 10.1016/j.jsv.2007.01.001.
Huseyin M. Ertunc, Kenneth A. Loparo, Hasan Ocak, "Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs)", International Journal of Machine Tools and Manufacture, Vol. 41, No. 9, pp. 1363 - 1384, 2001. DOI: 10.1016/S0890-6955(00)00112-7.
Hasan Ocak and Kenneth A. Loparo, "HMM-based fault detection and diagnosis scheme for rolling element bearings", Journal of Vibration and Acoustics, Vol. 127, No. 4, pp. 299 - 306, 2005. DOI: 10.1115/1.1924636.
Zhongjjie Shen, Zhengjja He, Xuefeng Chen, Chuang Sun, and Zhiwen Liu, "A monotonic degradation assessment index of rolling bearings using fuzzy support vector data description and running time", Sensors, Vol. 12, No. 8, pp. 10109 - 10135, 2012. DOI: 10.3390/s120810109.
Yan-Cheng Liu, Siyuan Liu, and Ning Wang, "Fully-tuned fuzzy neural network based robust adaptive tracking control of unmanned underwater vehicle with thruster dynamics", Neurocomputing, Vol. 196, pp. 1 - 13, 2016. DOI: 10.1016/j.neucom.2016.02.042.
Guo Liang, Li Naipeng, Jia Feng, Lei Yaguo, and Lin Jing, "A recurrent neural network based health indicator for remaining useful life prediction of bearings", Neurocomputing, Vol. 240, pp. 98 - 109, 2017. DOI: 10.106/j.neucom.2017.02.045.
Emmanuel Ramasso, Michèle Rombaut, and Noureddine Zerhouni, "Joint prediction of continuous and discrete states in time-series based on belief functions", IEEE Transactions on Cybernetics, Vol. 43, No. 1, pp. 37 - 50, 2013.
Kamran Javed, Rafael Gouriveau, and Noureddine Zerhouni, "A new multivariate approach for prognostics based on extreme learning machine and fuzzy clustering", IEEE Transactions on Cybernetics, Vol. 45, No. 12, pp. 2626 - 2639, 2015. DOI: 10.1109/TCYB.2014.2378056.c
Zhilian Liu, Ming J. Zuo, and Yong Qin, "Remaining useful life prediction of rolling element bearings based on health state assessment", Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Vol. 230, No. 2, pp. 314 - 330, 2016. DOI: 10.1177/0954406215590167.
Patricia Scanlon, Darren F. Kavanagh, and Francis Boland, "Residual life prediction of rotating machines using acoustic noise signals", IEEE Transactions on Instrumentation and Measurement, Vol. 62, pp. 95 - 108, 2012. DOI: 10.1109/TIM.2012.2212508.
Andrea Giantomassi, Francesco Ferracuti, Alessandro Benini, Gianluca Ippoliti, Sauro Longhi, and Antonio Petrucci, "Hidden Markov model for health estimation and prognosis of turbofan engines", ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 28 - 31 August, 2011. DOI: 10.1115/DETC2011-48174.
E. Ramasso and T. Denoeux, "Making use of partial knowledge about hidden states in HMMs: An approach based on belief functions", IEEE Transactions on Fuzzy Systems, Vol. 22, pp. 395 - 405, 2014.
F. Sloukia, R. Bouarfa, H. Medromi, and M. Wahbi, "Bearings prognostic using mixture of Gaussians hidden Markov model and support vector machine", International Journal of Network Security & Its Applications, Vol. 5, pp. 85 - 97, 2013.
P. Tamilselvan, Yibin Wang, and P. Wang, "Deep belief network based state classification for structural health diagnosis", 2012 IEEE Aerospace Conference, Montana, 3 - 10 March, 2012.
Liang Guo, Hongli Gao, Haifeng Huang, Xiang He, and ShiChao Li, "Multifeatures fusion and non-linear dimension reduction for intelligent bearing condition monitoring", Shock and Vibration, Vol. 2016, pp. 1 - 10, 2016. DOI: 10.1155/2016/4632562.
Jaouher Ben Ali, Brigitte Chebel-Morello, Lotfi Saidi, Simon Malinowski, and Farhat Fnaiech, "Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network", Mechanical Systems and Signal Processing, Vol. 56 - 57, pp. 150 - 172, 2015.
A. Soualhi, K. Medjaher, and N. Zerhouni, "Bearing health monitoring based on Hilber-Huang transform, support vector machine, and regression", IEEE Transactions on Instrumentation and Measurement, Vol. 64, pp. 52 - 62, 2015.
Hack-Eun Kim, Andy C.C. Tan, Joseph Mathew, and Byeong-Keun Choi, "Bearing fault prognosis based on health state probability estimation", Expert Systems with Application, Vol. 39, pp. 5200 - 5213, 2012.
Xiao-Sheng Si, Wenbin Wang, Chang-Hua Hu, Dong-Hua Zhou, "Remaining useful life estimation - A review on the statistical data driven approaches", European Journal of Operational Research, Vol. 213, pp. 1 - 14, 2011.
Y. Li, S. Billington, C. Zhang, T. Kurfess, S. Danyluk, and S. Liang, "Adaptive prognostics for rolling element bearing condition", Mechanical Systems and Signal Processing, Vol. 13, No. 1, pp. 103 - 113, 1999.
Yaguo Lei, Naipe Li, and Jing Lin, "A new method based on stochastic process models for machine remaining useful life prediction", IEEE Transactions on Instrumentation and Measurement, Vol. 65, pp. 2671 - 2684, 2016.
C. Sbarufatti, M. Corbetta, A. Manes, and M. Giglio, "Sequential Monte-Carlo sampling based on a committee of artificial neural networks for posterior state estimation and residual lifetime prediction", International Journal of Fatigue, Vol. 83, pp. 10 - 23, 2016.
Yongping Pan, Meng Joo Er, Xiang Li, Haoyong Yu, and Rafael Gouriveau, "Machine health condition prediction via online dynamic fuzzy neural networks", Engineering Applications of Artificial Intelligence, Vol. 35, pp. 105 - 113, 2014.
Lei Xiao, Xiaohui Chen, Xinghui Zhang, and Min Liu, "A novel approach for bearing remaining useful life estimation under neither failure nor suspension histories condition", Journal of Intelligent Manufacturing, Vol. 28, pp. 1893 - 1914, 2017. DOI: 10.1007/s10845-015-1077-x.
Lu Wang, Li Wang, Xue-zhi Wang, "Reliability estimation and remaining useful lifetime prediction for bearing based on proportional hazard model", Journal of Central South University, Vol. 22, pp. 4625 - 4633, 2015.
Ryad Zemouri and Rafael Gouriveau, "Towards accurate and reproducible predictions for prognostic: An approach combining a RRBF network and an autoRegressive model", IFAC Proceedings Volumes, Vol. 43, No. 3, pp. 140 - 145, 2010.
Arnaz Malhi, Ruqiang Yan, and Robert X. Gao, "Prognosis of defect propagation based on recurrent neural networks", IEEE Transactions on Instrumentation and Measurement, Vol. 60, pp. 703 - 711, 2011. DOI: 10.1109/TIM.2010.2078296.
Yu Peng, Hong Wang, Jianmin Wang, Datong Liu, and Xiyuan Peng, "A modified echo state network based remaining useful life estimation approach", 2012 IEEE Conference on Prognostics and Health Management, 2012.
Datong Liu, Wei Xie, Haitao Liao, and Yu Peng, "An integrated probabilistic approach to Lithium-Ion battery remaining useful life estimation", IEEE Transactions on Instrumentation and Measurement, Vol. 64, pp. 660 - 670, 2015.
IBM Cloud Education, “Recurrent neural networks”, 2020. [Online]. Available: https://www.ibm.com/cloud/learn/recurrent-neural-networks.
Samarjit Kar, Sujit Das, and Pijush Ghosh, "Applications of neuro fuzzy systems: A brief review and future outline", Applied Soft Computing, Vol. 15, pp. 243 - 259, 2014.
Jyh-Shing Roger Jang, Chuen-Tsai Sun, and Eiji Mizutani, Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. Prentice-Hall Inc., 1996.
Wilson Q. Wang, M. Farid Golnaraghi, and Fathy Ismail, "Prognosis of machine health condition using neuro-fuzzy systems", Mechanical Systems and Signal Processing, Vol. 18, No. 4, pp. 813 - 831, 2004. DOI: 10.1016/s0888-3270(03)00079-7.
Wilson Wang, "An adaptive predictor for dynamic system forecasting", Mechanical Systems and Signal Processing, Vol. 21, No. 2, pp. 809 - 823, 2007. DOI:10.1016/j.ymssp.2005.12.008.
Fagang Zhao, Jin Chen, Lei Guo, and Xinglin Li, "Neuro-fuzzy based condition prediction of bearing health", Journal of Vibration and Control, Vol. 15, No. 7, pp. 1079 - 1091, 2009. DOI:10.1177/1077546309102665.
Van Tung Tran, Bo-Suk Yang, and Andy Chit Chiow Tan, "Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems", Expert Systems with Applications, Vol. 36, No. 5, pp. 9378 - 9387, 2009. DOI: 10.1016/j.eswa.2009.01.007
Chaochao Chen, Bin Zhang, George Vachtsevanos, and Marcos E.Orchard, "Machine condition prediction based on adaptive neuro-fuzzy and high-order particle filtering", IEEE Transactions on Industrial Electronics, Vol. 58, No. 9, pp. 4353 - 4364, 2011.
Daniel Zurita Millan, Jesus A.Carino, Miguel Delgado-Prieto, and Juan Antonio Redondo, "Distributed neuro-fuzzy feature forecasting approach for condition monitoring", 2014 IEEE Emerging Technology and Factory Automation (ETFA), 2015.
Sajid Hussain and Hossam A.Gabbar, "Vibration analysis and time series prediction for wind turbine gearbox prognostics", International Journal of Prognostics and Health Management, Vol. 4, No. 3, 2013. DOI: 10.36001/ijphm.2013.v4i3.2144.
Vladimir N.Vapnik, "An overview of statistical learning theory", IEEE transactions on neural networks, Vol. 10, No. 5, pp. 988 - 999, 1999.
Shaojiang Dong and Tianhong Luo, "Bearing degradation process prediction based on the PCA and optimized LS-SVM model", Measurement, Vol. 46, No. 9, pp. 3143 - 3152, 2013. DOI: 10.1016/j.measurement.2013.06.038.
J. Carino, D. Zurita, Miguel Delgado Prieto, J. A. Redondo, and R. Romero-Troncoso, "Remaining useful life estimation of ball bearings by means of monotonic score calibration", IEEE International Conference on Industrial Technology (ICIT), 17 - 19 March 2015. DOI: 10.1109/ICIT.2015.7125351.
James K. Kimotho, Christoph Sondermann-Woelke, Tobias Meyer, and Walter Sextro, "Machinery prognostic method based on multi-class support vector machines and hybrid differential evolution particle swarm optimization", Chemical Engineering Transactions, Vol. 33, pp. 619 - 624, 2013.
Achmad Widodo and Bo-Suk Yang, "Machine health prognostics using survival probability and support vector machine", Expert Systems with Applications, Vol. 38, No. 7, pp. 8430 - 8437, 2011. DOI: 10.1016/j.eswa.2011.01.038.
Van Tung Tran and Bo-Suk Yang, "An intelligent condition-based maintenance platform for rotating machinery", Expert Systems with Applications, Vol. 39, No. 3, pp. 2977 - 2988, 2012. DOI:10.1016/j.eswa.2011.08.159.
T. Benkedjouh, K. Medjaher, N. Zerhouni, and S. Rechak, "Health assessment and life prediction of cutting tools based on support vector regression", Journal of Intelligent Manufacturing, Vol. 26, pp. 213 - 223, 2015. DOI: 10.1007/s10845-013-0774-6.
Jie Liu and Enrico Zio, "An adaptive online learning approach for support vector regression: online-SVR-FID", Mechanical Systems and Signal Processing, Vol. 76, pp. 796 - 809, 2016. DOI: 10.1016/j.ymssp.2016.02.056.
Emanuele Fumeo, Luca Oneto, Davide Anguita, "Condition based maintenance in railway transportation systems based on big data streaming analysis", Procedia Computer Science, Vol. 53, pp. 437 - 446, 2015. DOI: 10.1016/j.procs.2015.07.321.
W. J. Padgett and Meredith A. Tomlinson, "Inference from accelerated degradation and failure data based on Gaussian process models", Lifetime Data Analysis, Vol. 10, pp. 191 - 206, 2004.
Carl Edward Rasmussen, "Gaussian processes in machine learning", Advanced Lectures on Machine Learning, pp. 63 - 71, 2003. DOI: 10.1007/978-3-540-28650-9_4.
Kai Goebel, Bhaskar Saha, and Abhinav Saxena, "A comparison of three data-driven techniques for prognostics", 2008.
S.A. Aye, P.S. Heyns, "An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission", Mechanical Systems and Signal Processing, Vol. 84, pp. 485 - 498, 2017. DOI: 10.1016/j.ymssp.2016.07.039.
Man Shan Kan, Andy C.C. Tan, and Joseph Mathew, "A review on prognostic techniques for non-stationary and non-linear rotating systems", Mechanical Systems and Signal Processing, Vol. 62 - 63, pp. 1 - 20, 2015. DOI: 10.1016/j.ymssp.2015.02.016
1. The Author assigns all copyright in and to the article (the Work) to the Petrovietnam Journal, including the right to publish, republish, transmit, sell and distribute the Work in whole or in part in electronic and print editions of the Journal, in all media of expression now known or later developed.
2. By this assignment of copyright to the Petrovietnam Journal, reproduction, posting, transmission, distribution or other use of the Work in whole or in part in any medium by the Author requires a full citation to the Journal, suitable in form and content as follows: title of article, authors’ names, journal title, volume, issue, year, copyright owner as specified in the Journal, DOI number. Links to the final article published on the website of the Journal are encouraged.
