A framework to integrate novelty detection and remaining useful life prediction in Industry 4.0-based manufacturing systems

References

• Alasadi, S. A., and W. S. Bhaya. 2017. “Review of Data Preprocessing Techniques in Data Mining.” Journal of Engineering and Applied Sciences 12 (16): 4102–4107.
   Google Scholar
• Beden, S. M., S. Abdullah, and A. K. Ariffin. 2009. “Review of Fatigue Crack Propagation Models for Metallic Components.” European Journal of Scientific Research 28 (3): 364–397.
   Google Scholar
• Bever, K. 2007. “Understanding MIMOSA’s Open Standards for On-Board Health Assessment and Enterprise Application Integration.” In AIAA Infotech@ Aerospace 2007 Conference and Exhibit, 2925, Rohnert Park, California.
   Google Scholar
• Boutrot, J., Y. Giorgiutti, F. Rezende, and S. Barras. 2017. “Reliable and Accurate Determination of Life Extension for Offshore Units.” In Offshore Technology Conference. Offshore Technology Conference, Houston, Texas, USA, May 1–4.
   Google Scholar
• Caggiano, A. 2018. “Cloud-Based Manufacturing Process Monitoring for Smart Diagnosis Services.” International Journal of Computer Integrated Manufacturing 31 (7 (July 3)): 612–623. doi:https://doi.org/10.1080/0951192X.2018.1425552.
   Google Scholar
• Cattaneo, L., and M. Macchi. 2019. “A Digital Twin Proof of Concept to Support Machine Prognostics with Low Availability of Run-To-Failure Data.” IFAC-PapersOnLine 52 (no. 10): 37–42. doi:https://doi.org/10.1016/j.ifacol.2019.10.016.
   Google Scholar
• Chen, N., and K. L. Tsui. 2013. “Condition Monitoring and Remaining Useful Life Prediction Using Degradation Signals: Revisited.” IIE Transactions 45 (no. 9): 939–952. doi:https://doi.org/10.1080/0740817X.2012.706376.
   Google Scholar
• Cho, S., G. May, I. Tourkogiorgis, R. Perez, O. Lazaro, B. de la Maza, and D. Kiritsis. 2018. “A Hybrid Machine Learning Approach for Predictive Maintenance in Smart Factories of the Future.„ In Advances in Production Management Systems. Smart Manufacturing for Industry 4.0. APMS 2018. IFIP Advances in Information and Communication Technology. edited by I. Moon, G. Lee, J. Park, D. Kiritsis, G. von Cieminski, vol 536, Springer, Cham. https://doi.org/https://doi.org/10.1007/978-3-319-99707-0_39
   Google Scholar
• Cimino, C., E. Negri, and L. Fumagalli. 2019. “Review of Digital Twin Applications in Manufacturing.” Computers in Industry 113: 103–130. doi:https://doi.org/10.1016/j.compind.2019.103130.
   Google Scholar
• Cubillo, A., S. Perinpanayagam, and M. Esperon-Miguez. 2016. “A Review of Physics-Based Models in Prognostics: Application to Gears and Bearings of Rotating Machinery.” Advances in Mechanical Engineering 8 (no. 8): 1–21. doi:https://doi.org/10.1177/1687814016664660.
   Google Scholar
• Ćwikła, G. 2014. “Methods of Manufacturing Data Acquisition for Production Management-a Review.” Advanced Materials Research 837:: 618–623. Trans Tech Publ. doi:https://doi.org/10.4028/.scientific.net/AMR.837.618.
   Google Scholar
• Da, X. L., E. L. Xu, and L. Li. 2018. “Industry 4.0: State of the Art and Future Trends.” International Journal of Production Research 56 (8): 2941–2962. doi:https://doi.org/10.1080/00207543.2018.1444806.
   Web of Science ®Google Scholar
• Di Maio, F., K. L. Tsui, and E. Zio. 2012. “Combining Relevance Vector Machines and Exponential Regression for Bearing Residual Life Estimation.” Mechanical Systems and Signal Processing 31: 405–427. doi:https://doi.org/10.1016/j.ymssp.2012.03.011.
   Web of Science ®Google Scholar
• Flath, C. M., and N. Stein. 2018. “Towards a Data Science Toolbox for Industrial Analytics Applications.” Computers in Industry 94: 16–25. doi:https://doi.org/10.1016/j.compind.2017.09.003.
   Web of Science ®Google Scholar
• Fumagalli, L., L. Cattaneo, I. Roda, M. Macchi, and M. Rondi. 2019. “Data-Driven CBM Tool for Risk-Informed Decision-Making in an Electric Arc Furnace.” International Journal of Advanced Manufacturing Technology105: 595–608. https://doi.org/https://doi.org/10.1007/s00170-019-04189-w.
   Google Scholar
• Gao, R., L. Wang, R. Teti, D. Dornfeld, S. Kumara, M. Mori, and M. Helu. 2015. “Cloud-Enabled Prognosis for Manufacturing.” CIRP Annals 64 (2): 749–772. http://www.sciencedirect.com/science/article/pii/S000785061500150X.
   Web of Science ®Google Scholar
• García, S., S. Ramírez-Gallego, J. Luengo, J. M. Benítez, and F. Herrera. 2016. “Big Data Preprocessing: Methods and Prospects.” Big Data Analytics 1 (no. 1): 9. doi:https://doi.org/10.1186/s41044-016-0014-0.
   Google Scholar
• Garetti, M., L. Fumagalli, and E. Negri. 2015. “Role of Ontologies for CPS Implementation in Manufacturing.” Management and Production Engineering Review 6 (4): 26–32. doi:https://doi.org/10.1515/mper-2015-0033.
   Web of Science ®Google Scholar
• Gebraeel, N. Z., M. A. Lawley, R. Li, and J. K. Ryan. 2005. “Residual-Life Distributions from Component Degradation Signals: A Bayesian Approach.” IiE Transactions 37 (6): 543–557. doi:https://doi.org/10.1080/07408170590929018.
   Web of Science ®Google Scholar
• Glaessgen, E., and D. Stargel. 2012. “The Digital Twin Paradigm for Future NASA and US Air Force Vehicles.” In 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, Honolulu, Hawaii, April 23-26.
   Google Scholar
• Grubbs, F. E. 1969. “Procedures for Detecting Outlying Observations in Samples.” Technometrics 11 (1): 1–21. doi:https://doi.org/10.1080/00401706.1969.10490657.
   Web of Science ®Google Scholar
• Guillén, A. J., A. Crespo, M. Macchi, and J. Gómez. 2016. “On the Role of Prognostics and Health Management in Advanced Maintenance Systems.” Production Planning and Control 27 (12): 991–1004. doi:https://doi.org/10.1080/09537287.2016.1171920.
   Web of Science ®Google Scholar
• Heng, A., S. Zhang, A. C. C. Tan, and J. Mathew. 2009. “Rotating Machinery Prognostics: State of the Art, Challenges and Opportunities.” Mechanical Systems and Signal Processing 23 (3): 724–739. doi:https://doi.org/10.1016/j.ymssp.2008.06.009.
   Web of Science ®Google Scholar
• Hsieh, Y.-S., F.-T. Cheng, H.-C. Huang, C.-R. Wang, S.-C. Wang, and H.-C. Yang. 2012. “VM-Based Baseline Predictive Maintenance Scheme.” IEEE Transactions on Semiconductor Manufacturing 26 (1): 132–144. doi:https://doi.org/10.1109/TSM.2012.2218837.
   Google Scholar
• Huang, H.-Z., H.-K. Wang, Y.-F. Li, L. Zhang, and Z. Liu. 2015. “Support Vector Machine Based Estimation of Remaining Useful Life: Current Research Status and Future Trends.” Journal of Mechanical Science and Technology 29 (no. 1): 151–163. doi:https://doi.org/10.1007/s12206-014-1222-z.
   Google Scholar
• ISO 55000. 2014. Asset Management — Overview, Principles and Terminology. British Standard Institution, BSI. London: Standards Publication.
   Google Scholar
• Jardine, A. K. S., D. Lin, and D. Banjevic. 2006. “A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance.” Mechanical Systems and Signal Processing 20 (no. 7): 1483–1510. doi:https://doi.org/10.1016/j.ymssp.2005.09.012.
   Google Scholar
• Johnson, R. A., and D. W. Wichern. 2002. Applied Multivariate Statistical Analysis. NJ: Prentice hall Upper Saddle River.
   Google Scholar
• Kraft, J., and S. Kuntzagk. 2017. “Engine Fleet-Management: The Use of Digital Twins from a MRO Perspective.” In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition, June 26–30. Charlotte, North Carolina, USA: American Society of Mechanical Engineers Digital Collection.
   Google Scholar
• Kumar, A., R. B. Chinnam, and F. Tseng. 2019. “An HMM and Polynomial Regression Based Approach for Remaining Useful Life and Health State Estimation of Cutting Tools.” Computers & Industrial Engineering 128: 1008–1014. doi:https://doi.org/10.1016/j.cie.2018.05.017.
   Web of Science ®Google Scholar
• Le Son, K., M. Fouladirad, and A. Barros. 2016. “Remaining Useful Lifetime Estimation and Noisy Gamma Deterioration Process.” Reliability Engineering & System Safety 149: 76–87. doi:https://doi.org/10.1016/j.ress.2015.12.016.
   Web of Science ®Google Scholar
• Lee, G.-Y., M. Kim, Y.-J. Quan, M.-S. Kim, T. J. Y. Kim, H.-S. Yoon, S. Min, D.-H. Kim, J.-W. Mun, and J. W. Oh. 2018. “Machine Health Management in Smart Factory: A Review.” Journal of Mechanical Science and Technology 32 (3): 987–1009. doi:https://doi.org/10.1007/s12206-018-0201-1.
   Web of Science ®Google Scholar
• Lee, J., B. Bagheri, and H. A. Kao. 2015. “A Cyber-Physical Systems Architecture for Industry 4.0-Based Manufacturing Systems.” Manufacturing Letters 3: 18–23. doi:https://doi.org/10.1016/j.mfglet.2014.12.001.
   Google Scholar
• Lei, Y., N. Li, and J. Lin. 2016. “A New Method Based on Stochastic Process Models for Machine Remaining Useful Life Prediction.” IEEE Transactions on Instrumentation and Measurement 65 (12): 2671–2684. doi:https://doi.org/10.1109/TIM.2016.2601004.
   Web of Science ®Google Scholar
• Lei, Y., N. Li, L. Guo, N. Li, T. Yan, and J. Lin. 2018. “Machinery Health Prognostics: A Systematic Review from Data Acquisition to RUL Prediction.” Mechanical Systems and Signal Processing 104: 799–834.
   Web of Science ®Google Scholar
• Leturiondo, U., O. Salgado, L. Ciani, D. Galar, and M. Catelani. 2017. “Architecture for Hybrid Modelling and Its Application to Diagnosis and Prognosis with Missing Data.” Measurement 108: 152–162. doi:https://doi.org/10.1016/j.measurement.2017.02.003.
   Google Scholar
• Li, C., S. Mahadevan, Y. Ling, S. Choze, and L. Wang. 2017. “Dynamic Bayesian Network for Aircraft Wing Health Monitoring Digital Twin.” AIAA Journal 55 (3): 930–941. doi:https://doi.org/10.2514/1.J055201.
   Web of Science ®Google Scholar
• Li, N., N. Gebraeel, Y. Lei, L. Bian, and X. Si. 2019. “Remaining Useful Life Prediction of Machinery under Time-Varying Operating Conditions Based on a Two-Factor State-Space Model.” Reliability Engineering & System Safety 186: 88–100. doi:https://doi.org/10.1016/j.ress.2019.02.017.
   Google Scholar
• Li, Y., and U. Roy. 2015. “Challenges in Developing a Computational Platform to Integrate Data Analytics with Simulation-Based Optimization.” In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Boston, Massachusetts, USA: American Society of Mechanical Engineers Digital Collection.
   Google Scholar
• Liu, Q., M. Dong, W. Lv, X. Geng, and Y. Li. 2015. “A Novel Method Using Adaptive Hidden Semi-Markov Model for Multi-Sensor Monitoring Equipment Health Prognosis.” Mechanical Systems and Signal Processing 64: 217–232. doi:https://doi.org/10.1016/j.ymssp.2015.03.029.
   Web of Science ®Google Scholar
• Liu, -Y.-Y., M.-H. Hung, Y.-C. Lin, -C.-C. Chen, W.-L. Gao, and F.-T. Cheng. 2018. “A Cloud-Based Pluggable Manufacturing Service Scheme for Smart Factory.” In 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 1040–1045. Munich, Germany: IEEE. doi:https://doi.org/10.1109/COASE.2018.8560401
   Google Scholar
• Liu, Z., N. Meyendorf, and N. Mrad. 2018. “The Role of Data Fusion in Predictive Maintenance Using Digital Twin.” In AIP Conference Proceedings, 1949, 20023. Provo, Utah, USA: AIP Publishing LLC.
   Google Scholar
• Luo, J., A. Bixby, K. Pattipati, L. Qiao, M. Kawamoto, and S. Chigusa. 2003. “An Interacting Multiple Model Approach to Model-Based Prognostics.” In SMC’03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme-System Security and Assurance (Cat. No. 03CH37483), 1, 189–194 Washington, DC, 2003, pp. 189–194 vol.1, doi: https://doi.org/10.1109/ICSMC.2003.1243813. IEEE
   Google Scholar
• Luo, W., T. Hu, W. Zhu, and F. Tao. 2018. “Digital Twin Modeling Method for CNC Machine Tool.” In 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), 1–4. doi: https://doi.org/10.1109/ICNSC.2018.8361285.
   Google Scholar
• Moyne, J., and J. Iskandar. 2017. “Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing.” Processes 5 (no. 3): 39.
   Web of Science ®Google Scholar
• Napoleone, A., M. Macchi, and A. Pozzetti. 2020. “A Review on the Characteristics of Cyber-Physical Systems for the Future Smart Factories.” Journal of Manufacturing Systems 54: 305–335. doi:https://doi.org/10.1016/j.jmsy.2020.01.007.
   Google Scholar
• Nectoux, P., R. Gouriveau, K. Medjaher, E. Ramasso, B. Chebel-Morello, N. Zerhouni, and C. Varnier. 2012. PRONOSTIA: An Experimental Platform for Bearings Accelerated Degradation Tests. IEEE International Conference on Prognostics and Health Management, PHM '12, Jun 2012, Denver, Colorado, United States.
   Google Scholar
• Negri, E., L. Fumagalli, and M. Macchi. 2017. “A Review of the Roles of Digital Twin in CPS-Based Production Systems.” Procedia Manufacturing 11 (June): 939–948. doi:https://doi.org/10.1016/j.promfg.2017.07.198.
   Google Scholar
• Niu, G., B. S. Yang, and M. Pecht. 2010. “Development of an Optimized Condition-Based Maintenance System by Data Fusion and Reliability-Centered Maintenance.” Reliability Engineering and System Safety 95 (7 (July)): 786–796. doi:https://doi.org/10.1016/j.ress.2010.02.016.
   Google Scholar
• Pan, D., J.-B. Liu, and J. Cao. 2016. “Remaining Useful Life Estimation Using an Inverse Gaussian Degradation Model.” Neurocomputing 185: 64–72. doi:https://doi.org/10.1016/j.neucom.2015.12.041.
   Web of Science ®Google Scholar
• Patnaik, P., and X. Wu. 2018. “Linking MRO to Prognosis Based Health Management through Physics-of-Failures Understanding.” Proceedings of the ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. Volume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy, Oslo, Norway, June 11–15. V006T24A007. ASME. https://doi.org/https://doi.org/10.1115/GT2018-75395
   Google Scholar
• Pellegrino, J., M. Justiniano, A. Raghunathan, and B. A. Weiss. 2016. Measurement Science Roadmap for Prognostics and Health Management for Smart Manufacturing Systems. Roadmapping Workshop: Measurement Science for Prognostics and Health Management of Smart Manufacturing Systems, Gaithersburg, Maryland, USA. November 19-20.
   Google Scholar
• Penas, O., R. Plateaux, S. Patalano, and M. Hammadi. 2017. “Multi-Scale Approach from Mechatronic to Cyber-Physical Systems for the Design of Manufacturing Systems.” Computers in Industry 86: 52–69. doi:https://doi.org/10.1016/j.compind.2016.12.001.
   Web of Science ®Google Scholar
• Pham, H. T., B.-S. Yang, and T. T. Nguyen. 2012. “Machine Performance Degradation Assessment and Remaining Useful Life Prediction Using Proportional Hazard Model and Support Vector Machine.” Mechanical Systems and Signal Processing 32: 320–330. doi:https://doi.org/10.1016/j.ymssp.2012.02.015.
   Web of Science ®Google Scholar
• Pimentel, M. A. F., D. A. Clifton, L. Clifton, and L. Tarassenko. 2014. “A Review of Novelty Detection.” Signal Processing 99: 215–249. doi:https://doi.org/10.1016/j.sigpro.2013.12.026.
   Web of Science ®Google Scholar
• Polenghi, A., I. Roda, M. Macchi, and P. Trucco. 2019. “Risk Sources Affecting the Asset Management Decision-Making Process in Manufacturing: A Systematic Review of the Literature.” In Advances in Production Management Systems. Production Management for the Factory of the Future. APMS 2019. IFIP Advances in Information and Communication Technology, edited by F. Ameri, K. Stecke, G. von Cieminski, and D. Kiritsis, 274–282. Vol. 566. Cham: Springer.
   Google Scholar
• Polenghi, A., L. Fumagalli, and I. Roda. 2018. “Role of Simulation in Industrial Engineering: Focus on Manufacturing Systems.” in IFAC-PapersOnLine, 51, 496–501, 16th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2018 Bergamo, Italy, Elsevier B.V., June 11–13. doi:https://doi.org/10.1016/j.ifacol.2018.08.367
   Google Scholar
• Qi, Q., and F. Tao. 2018. “Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison.” IEEE Access 6: 3585–3593. doi:https://doi.org/10.1109/ACCESS.2018.2793265.
   Web of Science ®Google Scholar
• Raman, V., and M. Hassanaly. 2019. “Emerging Trends in Numerical Simulations of Combustion Systems.” Proceedings of the Combustion Institute 37 (2): 2073–2089. doi:https://doi.org/10.1016/j.proci.2018.07.121.
   Web of Science ®Google Scholar
• Rúbio, E. M., R. P. Dionísio, and P. M. B. Torres. 2018. “Industrial IoT Devices and Cyber-Physical Production Systems: Review and Use Case.”In Innovation, Engineering and Entrepreneurship. HELIX 2018. Lecture Notes in Electrical Engineering, vol 505, Guimarães, Portugal on June 27–29, 2018, Springer, Cham. https://doi.org/https://doi.org/10.1007/978-3-319-91334-6_40
   Google Scholar
• Saha, B., K. Goebel, and J. Christophersen. 2009. “Comparison of Prognostic Algorithms for Estimating Remaining Useful Life of Batteries.” Transactions of the Institute of Measurement and Control 31 (3–4): 293–308. doi:https://doi.org/10.1177/0142331208092030.
   Web of Science ®Google Scholar
• Shubenkova, K., A. Valiev, V. Shepelev, S. Tsiulin, and K. H. Reinau. 2018. “Possibility of Digital Twins Technology for Improving Efficiency of the Branded Service System.”2018 Global Smart Industry Conference (GloSIC), Chelyabinsk, 1-7. doi: https://doi.org/10.1109/GloSIC.2018.8570075.
   Google Scholar
• Si, X.-S., W. Wang, C.-H. Hu, and D.-H. Zhou. 2011. “Remaining Useful Life Estimation–a Review on the Statistical Data Driven Approaches.” European Journal of Operational Research 213 (no. 1): 1–14. doi:https://doi.org/10.1016/j.ejor.2010.11.018.
   Google Scholar
• Si, X.-S., W. Wang, C.-H. Hu, M.-Y. Chen, and D.-H. Zhou. 2013. “A Wiener-Process-Based Degradation Model with A Recursive Filter Algorithm for Remaining Useful Life Estimation.” Mechanical Systems and Signal Processing 35 (1–2): 219–237. doi:https://doi.org/10.1016/j.ymssp.2012.08.016.
   Web of Science ®Google Scholar
• Sikorska, J. Z., M. Hodkiewicz, and L. Ma. 2011. “Prognostic Modelling Options for Remaining Useful Life Estimation by Industry.” Mechanical Systems and Signal Processing 25 (5): 1803–1836. doi:https://doi.org/10.1016/j.ymssp.2010.11.018.
   Web of Science ®Google Scholar
• Sivalingam, K., M. Sepulveda, M. Spring, and P. Davies. 2018. “A Review and Methodology Development for Remaining Useful Life Prediction of Offshore Fixed and Floating Wind Turbine Power Converter with Digital Twin Technology Perspective.” In 2nd International Conference on Green Energy and Applications (ICGEA), 2018, 197-204. doi:https://doi.org/10.1109/ICGEA.2018.8356292.
   Google Scholar
• Tao, F., M. Zhang, Y. Liu, and A. Y. C. Nee. 2018. “Digital Twin Driven Prognostics and Health Management for Complex Equipment.” CIRP Annals 67 (1): 169–172. doi:https://doi.org/10.1016/j.cirp.2018.04.055.
   Web of Science ®Google Scholar
• Tao, T., E. Zio, and W. Zhao. 2018. “A Novel Support Vector Regression Method for Online Reliability Prediction under Multi-State Varying Operating Conditions.” Reliability Engineering & System Safety 177: 35–49. doi:https://doi.org/10.1016/j.ress.2018.04.027.
   Google Scholar
• Tian, Z. 2012. “An Artificial Neural Network Method for Remaining Useful Life Prediction of Equipment Subject to Condition Monitoring.” Journal of Intelligent Manufacturing 23 (no. 2): 227–237. doi:https://doi.org/10.1007/s10845-009-0356-9.
   Google Scholar
• Tuegel, E. 2012. “The Airframe Digital Twin: Some Challenges to Realization.” In 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, , Honolulu, Hawaii, April 23 - 26.
   Google Scholar
• Tygesen, U. T., K. Worden, T. Rogers, G. Manson, and E. J. Cross. 2019. “State-of-the-Art and Future Directions for Predictive Modelling of Offshore Structure Dynamics Using Machine Learning.” In: Pakzad S. (eds) Dynamics of Civil Structures, Volume 2. Conference Proceedings of the Society for Experimental Mechanics Series, Orlando, Florida, USA, 12-15, Springer, Cham. https://doi.org/https://doi.org/10.1007/978-3-319-74421-6_30
   Google Scholar
• Vachtsevanos, G., F. Lewis, M. Roemer, A. Hess, and B. Wu. 2006. Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Hoboken, New Jersey: John Wiley & Sons, Inc.
   Google Scholar
• Vathoopan, M., M. Johny, A. Zoitl, and A. Knoll. 2018. “Modular Fault Ascription and Corrective Maintenance Using a Digital Twin.” IFAC-PapersOnLine 51 (11): 1041–1046. doi:https://doi.org/10.1016/j.ifacol.2018.08.470.
   Google Scholar
• Večeř, P., M. Kreidl, and R. Šmíd. 2005. “Condition Indicators for Gearbox Condition Monitoring Systems.” Acta Polytechnica 45 (6), 35–43.
   Google Scholar
• Vogl, G. W., B. A. Weiss, and M. A. Donmez. 2014. “Standards for Prognostics and Health Management (PHM) Techniques within Manufacturing Operations.” In Annual Conference of the Prognostics and Health Management Society 2014, 576–588. National Institute of Standards and Technology Gaithersburg United States, Fort Worth, Texas, USA, 29 September – 2 October.
   Google Scholar
• Wang, H., H. Wang, G. Jiang, J. Li, and Y. Wang. 2019. “Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling.” Energies 12 (6): 984. doi:https://doi.org/10.3390/en12060984.
   Web of Science ®Google Scholar
• Wilder-James, E. 2016. “Breaking down Data Silos.” Harvard Business Review 12. https://hbr.org/2016/12/breaking-down-data-silos
   Google Scholar
• Yeo, B., and D. Grant. 2018. “Predicting Service Industry Performance Using Decision Tree Analysis.” International Journal of Information Management 38 (1): 288–300. doi:https://doi.org/10.1016/j.ijinfomgt.2017.10.002.
   Web of Science ®Google Scholar
• Zaccaria, V., M. Stenfelt, I. Aslanidou, and K. G. Kyprianidis. 2018. “Fleet Monitoring and Diagnostics Framework Based on Digital Twin of Aero-Engines.” Proceedings of the ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. Volume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy. Oslo, Norway, June 11–15, 2018. V006T05A021. ASME. https://doi.org/https://doi.org/10.1115/GT2018-76414
   Google Scholar
• Zemouri, R., and R. Gouriveau. 2010. “Towards Accurate and Reproducible Predictions for Prognostic: An Approach Combining a RRBF Network and an Autoregressive Model.” IFAC Proceedings Volumes 43 (3): 140–145. doi:https://doi.org/10.3182/20100701-2-PT-4012.00025.
   Google Scholar