KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI

Yıl 2021, , 256 - 276, 31.08.2021

Damla Rana Dündar , İnci Sarıçiçek , Eyüp Çinar , Ahmet Yazici

https://doi.org/10.31796/ogummf.873963

Cited By: 5

Öz

Endüstriyel sistemlerdeki makine arızalarını önleyerek üretimde oluşabilecek kesintilerden kaçınmak ve ilgili maliyetleri azaltmak etkin bir bakım yönetimi ile mümkündür. Etkin bakım yönetimi önleyici, düzeltici ve kestirimci bakım stratejilerinin yönetilmesi faaliyetlerini içermektedir. Son yıllarda, bilgisayar ve iletişim teknolojisindeki gelişmelerle kestirimci bakım stratejisi işletmeler için önem kazanmıştır. Kestirimci bakım kapsamında yapay zekâ teknikleri kullanılmaya ve geliştirilmeye başlamıştır. Bu çalışma, makine öğrenmesi (ML - machine learning) algoritmalarına dayalı kestirimci bakım (PdM - predictive maintenance) ile ilgili literatürdeki çalışmaların bir incelemesidir. İncelenen çalışmalar kullanılan makine öğrenmesi algoritmaları ve çalışmaların gerçekleştirildiği endüstri / ekipman kapsamında analiz edilmiştir. Literatürde kestirimci bakımda makine öğrenmesi algoritmalarını kullanan çalışmaları derleyen ve analiz eden bir çalışma bulunmadığından yapılan bu literatür çalışması ilgili konuda çalışacak araştırmacılara yol gösterecektir.

Anahtar Kelimeler

Kestirimci Bakım, Makine Öğrenmesi, Derin Öğrenme

Destekleyen Kurum

TÜBİTAK

Proje Numarası

118C252

Teşekkür

Bu yayın TÜBİTAK 2232 Uluslararası Lider Araştırmacılar Programından (Proje No 118C252) yararlanılarak oluşturulmuştur. Ancak yayın ile ilgili tüm sorumluluk yayının sahibine aittir. TÜBİTAK’tan alınan maddi destek, yayının içeriğinin bilimsel anlamda TÜBİTAK tarafından onaylandığı anlamına gelmez.

Kaynakça

• Ahmad, W., Khan, S. A., Islam, M. M. M. ve Kim, J.-M. (2018). A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models. Reliability Engineering & System Safety. Doi: https://doi.org/10.1016/j.ress.2018.02.003
• Ahmed, R., El Sayed, M., Gadsden, S. A., Tjong, J. ve Habibi, S. (2015). Automotive internal-combustion-engine fault detection and classification using artificial neural network techniques. IEEE Trans. Veh. Technol., 64(1), 21–33.
• Al-Dulaimi, A., Asif, A. ve Mohammadi, A. (2020). Noisy parallel hybrid model of NBGRU and NCNN architectures for remaining useful life estimation. Quality Engineering, 32(3), 371–387. Doi: https://doi.org/10.1080/08982112.2020.1754427
• Allah Bukhsh, Z., Saeed, A., Stipanovic, I. ve Doree, A. G. (2019). Predictive maintenance using tree-based classification techniques: A case of railway switches. Transportation Research Part C: Emerging Technologies, 101, 35–54. Doi: https://doi.org/10.1016/j.trc.2019.02.001
• Aydemir, G. ve Paynabar, K. (2019). Image-based Prognostics Using Deep Learning Approach. IEEE Transactions on Industrial Informatics, 1–1. Doi: https://doi.org/10.1109/tii.2019.2956220
• Baptista, M., Sankararaman, S., de Medeiros, I. P., Nascimento, C., Prendinger, H. ve Henriques, E. M. P. (2018). Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling. Computers & Industrial Engineering, 115, 41–53. Doi: https://doi.org/10.1016/j.cie.2017.10.033
• Barkana, B. D., Sarıçiçek, İ. ve Yıldırım, B. (2017). Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion. Knowledge-Based Systems, 118, 165–176. Doi: https://doi.org/10.1016/j.knosys.2016.11.022
• Ben Ali, J., Chebel-Morello, B., Saidi, L., Malinowski, S. ve Fnaiech, F. (2015). Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 56-57, 150–172. Doi: https://doi.org/10.1016/j.ymssp.2014.10.014
• Ben Ali, J., Fnaiech, N., Saidi, L., Chebel-Morello, B. ve Fnaiech, F. (2015). Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Applied Acoustics, 89, 16–27. Doi: https://doi.org/10.1016/j.apacoust.2014.08.016
• Benítez, P., Rodrigues, F., Talukdar, S., Gavilán, S., Varum, H. ve Spacone, E. (2018). Analysis of correlation between real degradation data and a carbonation model for concrete structures. Cement and Concrete Composites. Doi: https://doi.org/10.1016/j.cemconcomp.2018.09.019
• Borucka, A. ve Grzelak, M. (2019). Application of Logistic Regression for Production Machinery Efficiency Evaluation. Applied Sciences, 9(22), 4770. Doi: https://doi.org/10.3390/app9224770
• Bukhsh, Z. A., Stipanovic, I., Saeed, A. ve Doree, A. G. (2020). Maintenance intervention predictions using entity-embedding neural networks, Automation in Construction, 116, 2020.
• Caesarendra, W., Widodo, A. ve Yang, B. S. (2010). Application of relevance vector machine and logistic regression for machine degradation assessment, Mech. Syst. Signal Process., 24, 4, ss.1161–1171, 2010.
• Chang, C. - C. ve Lin, C. - J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2, 27.
• Chen, B., Liu, Y., Zhang, C. ve Wang, Z. (2020). Time Series Data for Equipment Reliability Analysis With Deep Learning. IEEE Access, 8, 105484–105493.
• Chen, C., Liu, Y., Wang, S., Sun, X., Di Cairano-Gilfedder, C., Titmus, S. ve Syntetos, A. A. (2020). Predictive maintenance using cox proportional hazard deep learning. Advanced Engineering Informatics, 44, 101054. Doi: https://doi.org/10.1016/j.aei.2020.101054
• Chen, C. - S. ve Chen, J. - S. (2011). Rotor fault diagnosis system based on sGA-based individual neural networks. Expert Systems with Applications, 38(9), 10822–10830. Doi: https://doi.org/10.1016/j.eswa.2011.02.074
• Chen, Z. ve Li, W. (2017). Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network. IEEE Transactions on Instrumentation and Measurement, 66(7), 1693–1702. Doi: https://doi.org/10.1109/tim.2017.2669947
• Cheng, J.C.P., Chen,W., Chen, K., Wang, Q. (2020). Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms. Autom. Constr. 2020, 112, 103087.
• Costello, J. J. A., West, G. M. ve McArthur, S. D. J. (2017). Machine Learning Model for Event-Based Prognostics in Gas Circulator Condition Monitoring. IEEE Transactions on Reliability, 66(4), 1048–1057. https://doi.org/10.1109/tr.2017.2727489
• Deng, L. (2014). A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans. Sig. Inf. Process, 3 (2014). Essien, A. E. ve Giannetti, C. (2020). A Deep Learning model for Smart Manufacturing using Convolutional LSTM Neural Network Autoencoders. IEEE Transactions on Industrial Informatics, 1–1. Doi: https://doi.org/10.1109/tii.2020.2967556
• Falamarzi, A., Moridpour, S., Nazem, M. ve Cheraghi, S. (2019). Prediction of tram track gauge deviation using artificial neural network and support vector regression. Australian Journal of Civil Engineering, 17(1), 63–71. Doi: https://doi.org/10.1080/14488353.2019.1616357
• Fernandes, S., Antunes, M., Santiago, A. R., Barraca, J. P., Gomes, D. ve Aguiar, R. L. (2020). Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance. Information, 11(4), 208. Doi: https://doi.org/10.3390/info11040208
• García Nieto, P. J., García-Gonzalo, E., Sánchez Lasheras, F. ve de Cos Juez, F. J. (2015). Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability. Reliability Engineering & System Safety, 138, 219.
• Gohel, H.A., Upadhyay, H., Lagos, L., Cooper, K., Sanzetenea, A. (2020). Predictive Maintenance Architecture Development for Nuclear Infrastructure using Machine Learning. Nucl. Eng. Technol. 2020, 52, 1436–1442.
• Han, C., Ma, T., Xu, G., Chen, S. ve Huang, R. (2020). Intelligent decision model of road maintenance based on improved weight random forest algorithm. International Journal of Pavement Engineering, 1–13. Doi:https://doi.org/10.1080/10298436.2020.1784418
• Harrell, F. (2001). Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer, New York.
• Hinton, G. E., Osindero, S. ve Teh, Y. - W. (2006). A fast learning algorithm for deep belief nets. Neural Comput., 18 (7) (2006) 1527–1554.
• Hoffmann, M.W., Wildermuth, S., Gitzel, R., Boyacı, A., Gebhardt, J., Kaul, H., Amihai, I., Forg, B., Suriyah, M., Leibfried, T., Stich, V., Hicking, J., Bremer, M., Kaminski, L., Beverungen, D., zur Heiden, P. ve Tornede, T. (2020). Integration of novel sensors and machine learning for predictive maintenance in medium voltage switchgear to enable the energy and mobility revolutions. Sensors, 2020, 20, 2099.
• Hu, H., Tang, B., Gong, X., Wei, W. ve Wang, H. (2017). Intelligent fault diagnosis of the high-speed train with big data based on deep neural networks. IEEE Trans. Ind. Inform., 13(4), 2106– 2116.
• Hsu, J. - Y., Wang, Y. - F., Lin, K. - C., Chen, M. - Y. ve Hsu, J. H. - Y. (2020). Wind Turbine Fault Diagnosis and Predictive Maintenance through Statistical Process Control and Machine Learning. IEEE Access, 1–1. Doi: https://doi.org/10.1109/access.2020.2968615
• Huang, D.-S. (1996). Systematic Theory of Neural Networks for Pattern Recognition, Publishing House of Electronic Industry of China, Beijing, 201.
• Ibarra-Zarate, D., Alonso-Valerdi, L. M., Chuya-Sumba, J., Velarde-Valdez, S. ve Siller, H. R. (2019). Prediction of Inconel 718 roughness with acoustic emission using convolutional neural network based regression. The International Journal of Advanced Manufacturing Technology. Doi: https://doi.org/10.1007/s00170-019-04378-7
• Janssens, O., Loccufier, M. ve Van Hoecke, S. (2019). Thermal imaging and vibration based multi-sensor fault detection for rotating machinery. IEEE Trans. Ind. Informat., 15(1), 434–444.
• Janssens, O., Van De Walle, R., Loccufier, M., Van Hoecke, S. (2018). Deep Learning for Infrared Thermal Image Based Machine Health Monitoring. IEEE/ASME Trans. Mechatron, 2018, 23, 151–159.
• Jardine, A. K., Lin, D. ve Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing, 20(7), 1483-1510.
• Jia, F., Lei, Y., Lin, J., Zhou, X. ve Lu, N. (2016). Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing, 72-73, 303–315. Doi: https://doi.org/10.1016/j.ymssp.2015.10.025
• Kaparthi, S., Bumblauskas, D. (2020). Designing predictive maintenance systems using decision tree-based machine learning techniques. Int. J. Qual. Reliab. Manag. 2020, 37, 659–686.
• Köksal, M. (2017). Bakım Planlaması. Ankara : Seçkin Yayıncılık.
• Krishnakumari, A., Elayaperumal, A., Saravanan, M. ve Arvindan, C. (2016). Fault diagnostics of spur gear using decision tree and fuzzy classifier. The International Journal of Advanced Manufacturing Technology, 89(9-12), 3487–3494.
• Kubat, M. (2017). An Introduction to Machine Learning. second edition. New York, NY, USA. Springer-Verlag.
• Kuhn, M. ve Kjell. J. (2013). Applied Predictive Modeling. New York : Springer.
• Kusiak, A. ve Verma, A., 2011, Prediction of Status Patterns of Wind Turbines: A Data-Mining Approach. Journal of Solar Energy Engineering, 133(1), 011008. Doi: https://doi.org/10.1115/1.4003188
• Lasisi, A. ve Attoh-Okine, N. (2018). Principal components analysis and track quality index: A machine learning approach. Transportation Research Part C: Emerging Technologies, 91, 230–248. Doi: https://doi.org/10.1016/j.trc.2018.04.001
• Lei, Y., Jia, F., Lin, J., Xing, S. ve Ding, S. X. (2016). An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans. Ind. Electron., 63(5), 3137–3147.
• Leo, B. (2001). Random forests. Kluwer Academic Publishers, 45, 5–32.
• Lee, J. S., Hwang, S. H., Choi, I. Y. ve Choi, Y. (2019). Estimation of crack width based on shape‐sensitive kernels and semantic segmentation. Structural Control and Health Monitoring. Doi: https://doi.org/10.1002/stc.2504
• Li, C., Sanchez, R.-V., Zurita, G., Cerrada, M., Cabrera, D. ve Vásquez, R. E. (2015). Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis. Neurocomputing, 168, 119–127.
• Li, G., Chen, H., Hu, Y., Wang, J., Guo, Y., Liu, J., Li, H., Huang, R., Lv, H., Li, J. (2018). An improved decision tree-based fault diagnosis method for practical variable refrigerant flow system using virtual sensor-based fault indicators. Applied Thermal Engineering, 129, 1292–1303. Doi: https://doi.org/10.1016/j.applthermaleng.2017.10.013
• Li, J. ve He, D. (2020). A Bayesian Optimization AdaBN-DCNN Method With Self-Optimized Structure and Hyperparameters for Domain Adaptation Remaining Useful Life Prediction. IEEE Access, 8, 41482–41501. Doi: https://doi.org/10.1109/access.2020s2976595
• Li, H., Parikh, D., He, Q., Qian, B., Li, Z., Fang, D. ve Hampapur, A. (2014). Improving rail network velocity: A machine learning approach to predictive maintenance. Transportation Research Part C: Emerging Technologies, 45, 17–26. Doi: https://doi.org/10.1016/j.trc.2014.04.013
• Li, H., Wang, Y., Zhao, P., Zhang, X. ve Zhou, P. (2014). Cutting tool operational reliability prediction based on acoustic emission and logistic regression model. Journal of Intelligent Manufacturing, 26(5), 923–931. Doi: https://doi.org/10.1007/s10845-014-0941-4
• Liao, L., Jin, W. ve Pavel, R. (2016). Enhanced Restricted Boltzmann Machine With Prognosability Regularization for Prognostics and Health Assessment. IEEE Transactions on Industrial Electronics, 63(11), 7076–7083. Doi: https://doi.org/10.1109/tie.2016.2586442
• Lin, Z. ve Liu, X. (2020). Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network. Energy, 117693. Doi: https://doi.org/10.1016/j.energy.2020.117693
• Louis, S.-Y. M., Nasiri, A., Bao, J., Cui, Y., Zhao, Y., Jin, J., Huang, X. ve Hu, J. (2020). Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission. Applied Sciences, 10(8), 2680. Doi: https://doi.org/10.3390/app10082680
• Lu, C., Wang, Z. - Y., Qin, W. - L. ve Ma, J. (2017). Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Processing, 130, 377–388. Doi: https://doi.org/10.1016/j.sigpro.2016.07.028
• Luo, B., Wang, H., Liu, H., Li, B. ve Peng, F. (2018). Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification. IEEE Transactions on Industrial Electronics, 1–1. Doi: https://doi.org/10.1109/tie.2018.280714
• Luo, W., Hu, T., Ye, Y., Zhang, C. ve Wei, Y. (2020). A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin. Robotics and Computer-Integrated Manufacturing, 65, 101974. Doi: https://doi.org/10.1016/j.rcim.2020.101974
• Mahamad, A. K., Saon, S. ve Hiyama, T. (2010). Predicting remaining useful life of rotatingmachinery based artificial neural network. Comput.Math. Appl., 60(4), 1078–1087.
• Malhi, A., Gao, R.X. (2004). PCA-based feature selection scheme for machine defect classification. IEEE Trans. Instrum. Meas., 53 (6) (2004) 1517–1525.
• Montero Jimenez, J. J., Schwartz, S., Vingerhoeds, R., Grabot, B. ve Salaün, M. (2020). Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics. Journal of Manufacturing Systems, 56, 539–557. Doi: https://doi.org/10.1016/j.jmsy.2020.07.008
• Nabizadeh, A. ve Tabatabai, H. (2020). Development of nonlinear probabilistic S-N curves using survival analysis techniques with application to steel bridges. International Journal of Fatigue, 105892. Doi: https://doi.org/10.1016/j.ijfatigue.2020.105892
• Nguyen, Khanh T.P., Medjaher, K. (2019). A new dynamic predictive maintenance framework using deep learning for failure prognostics, Reliability Engineering & System Safety, 188, 2019, 251-262.
• Özdamar, K. (2002). Paket Programlar ile İstatistiksel Veri Analizi. Cilt 1, 2.Baskı, Eskişehir : Kaan Kitabevi, 475-477.
• Özonur, D., Kılıç, D., Akdur, H. ve Bayrak, H. (2019). Temel Bileşenler Analizi ve Yanıt Yüzey Yöntemi Kullanılarak Gıda Sektöründe Çoklu Yanıtların Optimizasyonu. Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 12 (2019 ), 734-744.
• Pandya, D. H., Upadhyay, S. H. ve Harsha, S. P. (2014). Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform. Soft Comput., 18 (2) 255–266, 2014.
• Phillips, J., Cripps, E., Lau, J. W. ve Hodkiewicz, M. R. (2015). Classifying machinery condition using oil samples and binary logistic regression. Mechanical Systems and Signal Processing, 60-61, 316–325. Doi: https://doi.org/10.1016/j.ymssp.2014.12.020.
• Prieto, M. D., Cirrincione, G., Espinosa, A. G., Ortega J. A. ve Henao, H. (2013). Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE Trans. Ind. Electron., 60(8), 3398–3407.
• Prytz, R., Nowaczyk, S., Rögnvaldsson, T. ve Byttner, S. (2015). Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. Engineering Applications of Artificial Intelligence, 41, 139–150. Doi: https://doi.org/10.1016/j.engappai.2015.02.009
• Rengasamy, D., Jafari, M., Rothwell, B., Chen, X. ve Figueredo, G. P. (2020). Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management. Sensors, 20(3), 723. Doi: https://doi.org/10.3390/s20030723
• Ruiz-Sarmiento, J. - R., Monroy, J., Moreno, F. - A., Galindo, C., Bonelo, J. - M. ve Gonzalez-Jimenez, J. (2020). A predictive model for the maintenance of industrial machinery in the context of industry 4.0. Engineering Applications of Artificial Intelligence, 87, 103289. Doi: https://doi.org/10.1016/j.engappai.2019.103289
• Saimurugan, M., Ramachandran, K. I., Sugumaran, V. ve Sakthivel, N. R. (2011). Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine. Expert Systems with Applications, 38(4), 3819–3826. Doi: https://doi.org/10.1016/j.eswa.2010.09.042
• Santos, P., Maudes, J. ve Bustillo, A. (2018). Identifying maximum imbalance in datasets for fault diagnosis of gearboxes. J. Intell. Manuf., 29(2), 333–351.
• Scalabrini Sampaio, G., Vallim Filho, A. R. de A., Santos da Silva, L. ve Augusto da Silva, L. (2019). Prediction of Motor Failure Time Using An Artificial Neural Network. Sensors, 19(19), 4342. Doi: https://doi.org/10.3390/s19194342
• Sexton, T., Brundage, M. P., Hoffman, M. ve Morris, K. C. (2017). Hybrid datafication of maintenance logs from AI-assisted human tags. IEEE International Conference on Big Data (ss. 1769–1777).
• Shafi, U., Safi, A., Shahid, A. R., Ziauddin, S. ve Saleem, M. Q. (2018). Vehicle Remote Health Monitoring and Prognostic Maintenance System. Journal of Advanced Transportation, 2018, 1–10. Doi: https://doi.org/10.1155/2018/8061514.
• Shamayleh, A., Awad, M. ve Farhat, J. (2020). IoT Based Predictive Maintenance Management of Medical Equipment. Journal of Medical Systems. 44.
• Shao, H., Jiang, H., Wang, F. ve Zhao, H. (2017). An enhancement deep feature fusion method for rotating machinery fault diagnosis. Knowledge-Based Systems, 119, 200–220. Doi: https://doi.org/10.1016/j.knosys.2016.12.012
• Shao, H., Jiang, H., Lin, Y. ve Li, X. (2018). A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders. Mechanical Systems and Signal Processing, 102, 278–297. Doi: https://doi.org/10.1016/j.ymssp.2017.09.026
• Shao, H., Jiang, H., Zhao, H. ve Wang, F. (2017). A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mechanical Systems and Signal Processing, 95, 187–204. Doi: https://doi.org/10.1016/j.ymssp.2017.03.034
• Shin, J.-H., Jun, H.-B. ve Kim, J.-G. (2018). Dynamic control of intelligent parking guidance using neural network predictive control. Computers & Industrial Engineering, 120, 15–30. Doi: https://doi.org/10.1016/j.cie.2018.04.023
• Shrivastava, R., Mahalingam, H. ve Dutta, N. N. (2017). Application and evaluation of random forest classifier technique for fault detection in bioreactor operation. Chem. Eng. Commun., 204(5), 591–598.
• Si, X. - S., Wang, W., Hu, C.-H. ve Zhou, D. - H. (2011). Remaining useful life estimation – A review on the statistical data driven approaches. European Journal of Operational Research, 213(1), 1 - 14. Doi: https://doi.org/10.1016/j.ejor.2010.11.018
• Sikorska, J.Z., Hodkiewicz, M. ve Ma, L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Mech. Syst. Signal Process, 25, 1803–1836.
• Soualhi, A., Medjaher, K. ve Zerhouni, N. (2015). Bearing health monitoring based on Hilbert–Huang transform, support vector machine, and regression. IEEE Trans. Instrum. Meas., 64(1), 52–62.
• Su, C. J. ve Huang, S. F. (2018). Real-time big data analytics for hard disk drive predictive maintenance. Computers and Electrical Engineering, 71, 93–101.
• Susto, G. A., Member, S., Beghi, A. ve Luca, C. D. (2012). A predictive maintenance system for epitaxy processes based on filtering and prediction techniques. IEEE Transactions on Semiconductor Manufacturing, 25, 638–649.
• Susto, G. A., Schirru, A., Pampuri, S., McLoone, S. ve Beghi, A. (2015). Machine Learning for Predictive Maintenance: A Multiple Classifier Approach. IEEE Transactions on Industrial Informatics, 11(3), 812–820. Doi: https://doi.org/10.1109/tii.2014.2349359
• Thapliyal, P. ve Thakre, G. D. (2017). Correlation Study of Physicochemical, Rheological, and Tribological Parameters of Engine Oils. Advances in Tribology, 2017, 1–12. Doi: https://doi.org/10.1155/2017/1257607
• Tv, V., Diksha, Malhotra, P., Vig, L. ve Shroff, G. (2019). Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models.
• Utah, M. N. ve Jung, J. C. (2020). Fault state detection and remaining useful life prediction in AC powered solenoid operated valves based on traditional machine learning and deep neural networks. Nuclear Engineering and Technology. Doi: https://doi.org/10.1016/j.net.2020.02.001
• Wakiru, J., Pintelon, L., Chemweno, P. Ve Muchiri, P. (2017). A lubricant condition monitoring approach for maintenance decision support – A data exploratory case study. Maint. Forum, (2017) 69–82.
• Wakiru, J. M., Pintelon, L., Muchiri, P. N. ve Chemweno, P. K. (2019). A review on lubricant condition monitoring information analysis for maintenance decision support. Mechanical systems and signal processing, 118, 108-132.
• Wang, Q., Bu, S. ve He, Z. (2020). Achieving Predictive and Proactive Maintenance for High-Speed Railway Power Equipment with LSTM-RNN. IEEE Transactions on Industrial Informatics, 1–1. Doi: https://doi.org/10.1109/tii.2020.2966033
• Wang, L., Zhang, Z., Long, H., Xu, J. ve Liu, R. (2017). Wind Turbine Gearbox Failure Identification With Deep Neural Networks. IEEE Transactions on Industrial Informatics, 13(3), 1360–1368. Doi: https://doi.org/10.1109/tii.2016.2607179
• Wu, Z., Guo, Y., Lin, W., Yu, S. ve Ji, Y. (2018). A Weighted Deep Representation Learning Model for Imbalanced Fault Diagnosis in Cyber-Physical Systems. Sensors. 18, 1096. Doi: https://doi.org/10.3390/s18041096
• Wu, T.-L., Sari, D. Y., Lin, B.-T. ve Chang, C.-W. (2017). Monitoring of punch failure in micro-piercing process based on vibratory signal and logistic regression. Int. J. Adv. Manuf. Technol., 93, 5–8, 2447–2458.
• Xia, M., Zheng, X., Imran, M. ve Shoaib, M. (2020). Data-driven prognosis method using hybrid deep recurrent neural network. Applied Soft Computing, 106351. Doi: https://doi.org/10.1016/j.asoc.2020.106351
• Xu, Y., Sun, Y., Liu, X. ve Zheng, Y. (2019). A Digital-Twin-Assisted Fault Diagnosis using Deep Transfer Learning. IEEE Access, 1–1. Doi: https://doi.org/10.1109/access.2018.2890566
• Yan, J. ve Lee, J. (2005). Degradation Assessment and Fault Modes Classification Using Logistic Regression. Journal of Manufacturing Science and Engineering, 127(4), 912. Doi: https://doi.org/10.1115/1.1962019.
• Yang, D., Liu, Y., Li, S., Li, X. ve Ma, L. (2015). Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm. Mechanism and Machine Theory, 90, 219–229. Doi: https://doi.org/10.1016/j.mechmachtheory.2015.03.013
• Yeh, C. - H., Lin, M. - H., Lin, C. - H., Yu, C. - E. ve Chen, M .- J. (2019). Machine Learning for Long Cycle Maintenance Prediction of Wind Turbine. Sensors, 19(7), 1671. Doi: https://doi.org/10.3390/s19071671
• You, D., Gao, X. ve Katayama, S. (2015). WPD-PCA-based laser welding process monitoring and defects diagnosis by using FNN and SVM. IEEE Trans. Ind. Electron., 62(1), 628–636.
• Yu, J. (2017). Tool condition prognostics using logistic regression with penalization and manifold regularization. Appl. Soft Comput., 64, 453–467.
• Yu, T., Zhu, C., Chang, Q. ve Wang, J. (2019). Imperfect corrective maintenance scheduling for energy efficient manufacturing systems through online task allocation method. Journal of Manufacturing Systems, 53, 282–290. Doi: https://doi.org/10.1016/j.jmsy.2019.11.002
• Zhang, X., Liang, Y., Zhou, J. ve Zang, Y. (2015). A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement, 69, 164–179. Doi: https://doi.org/10.1016/j.measurement.2015.03.017
• Zhang, S., Liu, C., Su, S., Han, Y. ve Li, X. (2018). A feature extraction method for predictive maintenance with time-lagged correlation-based curve-registration model. International Journal of Network Management. Doi: https://doi.org/10.1002/nem.2025
• Zhang, R., Peng, Z., Wu, L., Yao, B. ve Guan, Y. (2017). Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence. Sensors, 17(3), 549. Doi: https://doi.org/10.3390/s17030549
• Zhang, W., Yang, D. ve Wang, H. (2019). Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey. IEEE Systems Journal, 1–15. Doi: https://doi.org/10.1109/jsyst.2019.2905565
• Zhang, B., Zhang, S. ve Li, W. (2019). Bearing performance degradation assessment using long short-term memory recurrent network. Computers in Industry, 106, 14–29. Doi: https://doi.org/10.1016/j.compind.2018.12.016
• Zhang, H., Zhang, Q., Shao, S., Niu, T. ve Yang, X. (2020). Attention-based LSTM network for rotatory machine remaining useful life prediction. IEEE Access, 1–1. Doi: https://doi.org/10.1109/access.2020.3010066