KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI

Damla Rana DÜNDAR, (Eskişehir Osmangazi Üniversitesi, Mühendislik-Mimarlık Fakültesi, Endüstri Mühendisliği, Eskişehir, Türkiye)
İnci SARIÇİÇEK, (Eskişehir Osmangazi Üniversitesi, Mühendislik-Mimarlık Fakültesi, Endüstri Mühendisliği, Eskişehir, Türkiye)
Eyüp ÇINAR, (Eskişehir Osmangazi Üniversitesi, Mühendislik-Mimarlık Fakültesi, Endüstri Mühendisliği, Eskişehir, Türkiye)
Ahmet YAZICI (Eskişehir Osmangazi Üniversitesi, Mühendislik-Mimarlık Fakültesi, Endüstri Mühendisliği, Eskişehir, Türkiye)
Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi (online)
16 0
Yıl: 2021 Cilt: 29 Sayı: 2 Sayfa Aralığı: 256 - 276 Metin Dili: Türkçe İndeks Tarihi: 20-11-2021

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. Sensör ve bilgisayar teknolojisindeki son gelişmelerle kestirimci bakım çalışmaları işletmeler için önem kazanmıştır. Veriye Dayalı makine öğrenimi algoritmalarının yardımıyla güçlendirilen kestirimci bakım sistemleri, yeni üretim sistemlerinin bir parçası olarak geliştirilir. Bu çalışma, makine öğrenmesi algoritmalarına dayalı kestirimci bakım ile ilgili literatürün bir incelemesidir. 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 çok az çalışma olup bu çalışma ilgili konuda çalışacak araştırmacılara yol gösterecektir.
Anahtar Kelime:

MACHINE LEARNING IN PREDICTIVE MAINTENANCE: LITERATURE RESEARCH

Öz:
By preventing machine failures in industrial systems, avoiding interruptions that mayoccur in production is possible with an effective maintenance management. Effectivemaintenance management includes the activities of managing preventive, corrective andpredictive maintenance sites. With the latest developments facilitated by sensor andcomputer technologies, predictive maintenance studies have gained importance forbusinesses. Empowered with the help of data-driven machine learning algorithms,predictive maintenance sytems are developed as a part of new manufacturing sytems.This study is a review of the literature on predictive maintenance based on machinelearning algorithms. The studies are analyzed within the scope of employed machinelearning algorithms and related industry/equipment where the studies are carried out.There are very few work in the literature that compile and analyze studies using machinelearning algorithms in predictive maintenance, so this study will guide researchers whowill work on the relevant subject.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Derleme Erişim Türü: Erişime Açık
  • 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, 64, 67-76. 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 Transactions on Vehicular Technology, 64(1), 21–33. Doi: https://doi.org/10.1109/TVT.2014.2317736
  • 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. KnowledgeBased 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
  • Biau, G. ve Scornet, E. (2016). A random forest guided tour. TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 25, 197–227.
  • 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. Doi: https://doi.org/10.1016/j.autcon.2020.103202
  • Caesarendra, W., Widodo, A. ve Yang, B. S. (2010). Application of relevance vector machine and logistic regression for machine degradation assessment, Mechanical Systems and Signal Processing, 24(4), 1161–1171. Doi: https://doi.org/10.1016/ j.ymssp.2009.10.011
  • Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. D. P., Basto, J. P., ve Alcalá, S. G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024. Doi: https://doi.org/10.1016/j.cie.2019.106024
  • Chang, C.- C. ve Lin, C.- J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(27), 1-27.
  • 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. Doi: https://doi.org/10.1109/ACCESS.2020.3000006
  • 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. ve Wang, Q. (2020). Datadriven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms. Automation in Construction, 2020, 112, 103087. Doi: https://doi.org/10.1016/j.autcon.2020.103087
  • Cinar, Z.M. Nuhu, A.A., Zeeshan, Q., Korhan, O., Asmael, M. ve Safaei, B. (2020). Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0, Sustainability, 12(19), 8211. doi: https://doi.org/10.3390/su12198211.
  • 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). Doi: https://doi.org/10.1017/ATSIP.2014.4
  • 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, 16(9), 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. Doi: https://doi.org/10.1016/j.ress.2015.02.001
  • Gohel, H.A., Upadhyay, H., Lagos, L., Cooper, K. Ve Sanzetenea, A. (2020). Predictive Maintenance Architecture Development for Nuclear Infrastructure using Machine Learning. Nuclear Engineering and Technology, 52(2020), 1436–1442. Doi: https://doi.org/10.1016/j.net.2019.12.029
  • 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
  • Hinton, G. E., Osindero, S. ve Teh, Y.- W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 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, 20(7), 2099. Doi: https://doi.org/10.3390/s20072099
  • 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 Transactions on Industrial Informatics, 13(4), 2106– 2116. Doi: https://doi.org/10.1109/TII.2017.2683528
  • 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, 8, 23427 – 23439. 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, 105, 1609–1621.
  • Jahnke, P., (2015). Machine Learning Approaches for Failure Type Detection and Predictive Maintenance (yüksek lisans tezi), Technische Universität Darmstadt, Almanya.
  • 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. Doi: https://doi.org/10.1016/j.ymssp. 2005.09.012
  • Janssens, O., Loccufier, M. ve Van Hoecke, S. (2019). Thermal imaging and vibration based multi-sensor fault detection for rotating machinery. IEEE Transactions on Industrial Informatics, 15(1), 434– 444. Doi: https://doi.org/10.1109/TII.2018. 2873175
  • Janssens, O., Van De Walle, R., Loccufier, M., Van Hoecke, S. (2018). Deep Learning for Infrared Thermal Image Based Machine Health Monitoring. IEEE/ASME Transactions on Mechatronics, 2018, 23, 151–159. Doi: https://doi.org/10.1109/TMECH.2017.2722479
  • 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 treebased machine learning techniques. International Journal of Quality & Reliability Management, 37(4), 659–686.
  • Köksal, M. (2017). Bakım Planlaması. Ankara: Seçkin Yayıncılık.
  • Krishnakumari, A., Elayaperumal, A., Saravanan, M. ve Arvindan, C. (2017). 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. Springer-Verlag.New York, NY, USA. 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 Transactions on Industrial Electronics, 63(5), 3137–3147. Doi: https://doi.org/10.1109/TIE.2016.2519325
  • Leo, B. (2001). Random forests. Machine Leraning, 45, 5– 32. Doi: https://doi.org/10.1023/A:1010933404324
  • 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, 37(4). 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: An International Journal, 168, 119– 127. Doi: https://doi.org/10.1016/ j.neucom.2015.06.008
  • Li, G., Chen, H., Hu, Y., Wang, J., Guo, Y., Liu, J., Li, H., Huang, R., Lv, H. ve 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 AdaBNDCNN Method With Self-Optimized Structure and Hyperparameters for Domain Adaptation Remaining Useful Life Prediction. IEEE Access, 8, 41482–41501.
  • 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.
  • 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, 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
  • 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, 66(1).
  • Mahamad, A. K., Saon, S. ve Hiyama, T. (2010). Predicting remaining useful life of rotatingmachinery based artificial neural network. Computers & Mathematics with Applications, 60(4), 1078–1087. Doi: https://doi.org/10.1016/j.camwa.2010.03.065
  • Malhi, A. ve Gao, R.X. (2004). PCA-based feature selection scheme for machine defect classification. IEEE Transactions on Instrumentation and Measurement, 53 (6) (2004) 1517–1525. Doi: https://doi.org/10.1109/TIM.2004.834070
  • Montero Jimenez, J. J., Schwartz, S., Vingerhoeds, R., Grabot, B. ve Salaün, M. (2020). Towards multimodel 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
  • Namuduri, S. (2020). Review-Deep Learning Methods for Sensor Based Predictive Maintenance and Future Perspectives for Electrochemical Sensors, Journal of the Electrochemical Society, Volume: 167 Issue: 3, doi: https://doi.org/10.1149/1945-7111/ab67a8
  • Nguyen, K.T.P. ve Medjaher, K. (2019). A new dynamic predictive maintenance framework using deep learning for failure prognostics, Reliability Engineering & System Safety, 188, 2019, 251-262.
  • 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. Doi: https://doi.org/10.18185/erzifbed.485762
  • Öztanır, O. (2018). Makine Öğrenmesi Kullanılarak Kestirimci Bakim (Yüksek Lisans Tezi). Elektrik ve Elektronik Anabilim Dalı, Hacettepe Üniversitesi, Danışman: Prof. Dr. Ali Ziya Alkar.
  • 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 Computing, 18 (2) 255–266, 2014.
  • Pench, M., Vrchota, J. ve Bednár, J. (2021). Predictive Maintenance and Intelligent Sensors in Smart Factory: Review, Sensors, 21, Doi:https://doi.org/10.3390/s21041470
  • 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.
  • 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 Transactions on Industrial Electronics, 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 SensorBased 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. Journal of Intelligent Manufacturing, 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. Chemical Engineering Communications, 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. Mechanical Systems and Signal Processing, 25, 1803–1836. Doi: https://doi.org/10.1016/j.ymssp.2010.11.018
  • Soualhi, A., Medjaher, K. ve Zerhouni, N. (2015). Bearing health monitoring based on Hilbert–Huang transform, support vector machine, and regression. IEEE Transactions on Instrumentation and Measurement, 64(1), 52–62. Doi: https://doi.org/10.1109/TIM.2014.2330494
  • 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(4), 638–649. Doi: https://doi.org/10.1109/TSM.2012.2209131
  • 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. Erişim adresi: https://www.researchgate.net/publication/331986412_
  • 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. Maintenance 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(4), 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. The International Journal of Advanced Manufacturing Technology, 93, 5–8, 2447–2458.
  • Xia, M., Zheng, X., Imran, M. ve Shoaib, M. (2020). Datadriven 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-TwinAssisted Fault Diagnosis using Deep Transfer Learning. IEEE Access, 7, 19990-19999. 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.2015.0 3.013
  • 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 Transactions on Industrial Electronics, 62(1), 628–636. Doi: https://doi.org/10.1109/TIE.2014.2319216
  • Yu, J. (2018). Tool condition prognostics using logistic regression with penalization and manifold regularization. Applied Soft Computing, 64, 454–467. https://doi.org/10.1016/j.asoc.2017.12.042
  • 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, 28(5). 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, 13(3), 2213–2227. 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, 8, 132188-132199. Doi: https://doi.org/10.1109/ access.2020.3010066
APA DÜNDAR D, Sarıçiçek İ, Cinar E, YAZICI A (2021). KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI. , 256 - 276.
Chicago DÜNDAR DAMLA RANA,Sarıçiçek İnci,Cinar Eyup,YAZICI Ahmet KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI. (2021): 256 - 276.
MLA DÜNDAR DAMLA RANA,Sarıçiçek İnci,Cinar Eyup,YAZICI Ahmet KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI. , 2021, ss.256 - 276.
AMA DÜNDAR D,Sarıçiçek İ,Cinar E,YAZICI A KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI. . 2021; 256 - 276.
Vancouver DÜNDAR D,Sarıçiçek İ,Cinar E,YAZICI A KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI. . 2021; 256 - 276.
IEEE DÜNDAR D,Sarıçiçek İ,Cinar E,YAZICI A "KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI." , ss.256 - 276, 2021.
ISNAD DÜNDAR, DAMLA RANA vd. "KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI". (2021), 256-276.
APA DÜNDAR D, Sarıçiçek İ, Cinar E, YAZICI A (2021). KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI. Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi (online), 29(2), 256 - 276.
Chicago DÜNDAR DAMLA RANA,Sarıçiçek İnci,Cinar Eyup,YAZICI Ahmet KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI. Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi (online) 29, no.2 (2021): 256 - 276.
MLA DÜNDAR DAMLA RANA,Sarıçiçek İnci,Cinar Eyup,YAZICI Ahmet KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI. Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi (online), vol.29, no.2, 2021, ss.256 - 276.
AMA DÜNDAR D,Sarıçiçek İ,Cinar E,YAZICI A KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI. Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi (online). 2021; 29(2): 256 - 276.
Vancouver DÜNDAR D,Sarıçiçek İ,Cinar E,YAZICI A KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI. Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi (online). 2021; 29(2): 256 - 276.
IEEE DÜNDAR D,Sarıçiçek İ,Cinar E,YAZICI A "KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI." Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi (online), 29, ss.256 - 276, 2021.
ISNAD DÜNDAR, DAMLA RANA vd. "KESTİRİMCİ BAKIMDA MAKİNE ÖĞRENMESİ: LİTERATÜR ARAŞTIRMASI". Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi (online) 29/2 (2021), 256-276.
Sayfa Oluşturulma Tarihi
23-04-2024 20:12

İLETİŞİM

TÜBİTAK ULAKBİM TR Dizin

Yüzüncüyıl, İşçi Blokları Mahallesi

Muhsin Yazıcıoğlu Caddesi No:51/C

06530 Çankaya / ANKARA +90 (312) 298 92 00

trdizin@tubitak.gov.tr

TÜBİTAK ULAKBİM Ulusal Akademik Ağ ve Bilgi Merkezi Cahit Arf Bilgi Merkezi © Tüm Hakları Saklıdır. Gizlilik Politikası Kullanım Şartları