KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI

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

doi:10.31796/ogummf.873963

KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI

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

Ayrıntılar

Birincil Dil

Türkçe

Konular

Bilgisayar Yazılımı

Bölüm

Derleme

Yazarlar

Damla Rana Dündar ^*
0000-0002-9286-2817
Türkiye

İnci Sarıçiçek
0000-0002-3528-7342
Türkiye

Eyüp Çinar
0000-0003-3189-7247
Türkiye

Ahmet Yazici
0000-0001-5589-2032
Türkiye

Yayımlanma Tarihi

31 Ağustos 2021

Gönderilme Tarihi

3 Şubat 2021

Kabul Tarihi

13 Haziran 2021

Yayımlandığı Sayı

Yıl 2021 Cilt: 29 Sayı: 2

DOI

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

IZ

https://izlik.org/JA85PZ77EM

APA

Dündar, D. R., Sarıçiçek, İ., Çinar, E., & Yazici, A. (2021). KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 29(2), 256-276. https://doi.org/10.31796/ogummf.873963

AMA

1.Dündar DR, Sarıçiçek İ, Çinar E, Yazici A. KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI. ESOGÜ Müh Mim Fak Derg. 2021;29(2):256-276. doi:10.31796/ogummf.873963

Chicago

Dündar, Damla Rana, İnci Sarıçiçek, Eyüp Çinar, ve Ahmet Yazici. 2021. “KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 29 (2): 256-76. https://doi.org/10.31796/ogummf.873963.

EndNote

Dündar DR, Sarıçiçek İ, Çinar E, Yazici A (01 Ağustos 2021) KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 29 2 256–276.

IEEE

[1]D. R. Dündar, İ. Sarıçiçek, E. Çinar, ve A. Yazici, “KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI”, ESOGÜ Müh Mim Fak Derg, c. 29, sy 2, ss. 256–276, Ağu. 2021, doi: 10.31796/ogummf.873963.

ISNAD

Dündar, Damla Rana - Sarıçiçek, İnci - Çinar, Eyüp - Yazici, Ahmet. “KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 29/2 (01 Ağustos 2021): 256-276. https://doi.org/10.31796/ogummf.873963.

JAMA

1.Dündar DR, Sarıçiçek İ, Çinar E, Yazici A. KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI. ESOGÜ Müh Mim Fak Derg. 2021;29:256–276.

MLA

Dündar, Damla Rana, vd. “KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, c. 29, sy 2, Ağustos 2021, ss. 256-7, doi:10.31796/ogummf.873963.

Vancouver

1.Damla Rana Dündar, İnci Sarıçiçek, Eyüp Çinar, Ahmet Yazici. KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI. ESOGÜ Müh Mim Fak Derg. 01 Ağustos 2021;29(2):256-7. doi:10.31796/ogummf.873963

KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI

KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI

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DOI

IZ

Kaynak Göster

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