Development of Digital Twin for Reciprocating Compressor Using Machine Learning Methodic
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Publisher
Springer Nature Switzerland
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https://link.springer.com/content/pdf/10.1007/978-3-031-67192-0_16
Reference10 articles.
1. Perazzo Morillo, A.H.V., Kurka, P.R.G., Bittencourt, M.L.: dynamics analysis of reciprocating compressor crankshafts: vol. 2. In: book: Proceedings of the 10th International Conference on Rotor Dynamics – IFToMM (2019). https://doi.org/10.1007/978-3-319-99268-6_34
2. Oral, A., UI Haque, U., Yalcin, O., Lazoglu, I.: Parametric analysis in crankshaft design for the reciprocating compressors. In: International Conference on Compressors and their Systems (2024). https://doi.org/10.1007/978-3-031-42663-6_34
3. Li, X., Ren, P., Zhang, Z., Jia, X., Peng, X.: A P-V diagram based fault identification for compressor valve by means of linear discrimination analysis. Machines 10, 53 (2022). https://doi.org/10.3390/machines10010053
4. Yusupbekov, N., Abdurasulov, F., Adilov, F., Ivanyan, A.: Concepts and methods of “Digital Twins” models creation in industrial asset performance management systems: In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I., Cebi, S., Tolga, A. (eds.) Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. INFUS 2020. Advances in Intelligent Systems and Computing, vol. 1197, pp. 1589–1595. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-51156-2_185
5. Yusupbekov, N., Adilov, F., Ivanyan, A.: Development of digital twin for centrifugal rotating equipment assets. In: Kahraman, C., et al. (eds.) INFUS 2022, LNNS 505, pp. 446–455 (2022). https://doi.org/10.1007/978-3-031-09176-6_51
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