References
- [1] Zhou, H., Mao, Y., Zhang, Q., Zhao, C., Qi, D., & Diao, Q. “Vibro-acoustics of a pipeline centrifugal compressor part I. Experimental study”, Applied Acoustics,131, pp. 112 – 128, 2018. DOI: 10.1016/j.apacoust.2017.10.011
- [2] Liao, H. J., & Huang, S. Z. “The fault diagnosis for centrifugal compressor based on time series analysis with neutral network”, In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), 6, pp 159, 2010. IEEE. DOI:10.1109/ICACTE.2010.5579406
- [3] Žiaran, S., Chlebo, O., Šooš, Ľ. “Determination of Bearing Quality Using Frequency Vibration Analysis”, Strojnícky časopis – Journal of Mechanical Engineering 71 (2), pp. 343 – 350, 2021. DOI: 10.2478/scjme-2021-0040
- [4] Liang, X., Duan, F., Mba, D., & Ian, B. “Centrifugal Compressor Diagnosis Using Kernel PCA and Fuzzy Clustering”, In Asset Intelligence through Integration and Interoperability and Contemporary Vibration Engineering Technologies, pp. 373-381. Springer, Cham. 2019. DOI: 10.1007/978-3-319-95711-1_37
- [5] Safiyullah, F., Sulaiman, S. A., Naz, M. Y., Jasmani, M. S., & Ghazali, S. M. A. “Prediction on performance degradation and maintenance of centrifugal gas compressors using genetic programming”, Energy 158, pp. 485 – 494, 2018. DOI: 10.1016/j.energy.2018.06.051
- [6] White, M. F. “Expert systems for fault diagnosis of machinery”, Measurement 9 (4), pp. 163 – 171, 1991. DOI: 10.1016/0263-2241(91)90012-F
- [7] Raposo, H., Farinha, J. T., Fonseca, I., & Galar, D. “Predicting condition based on oil analysis–A case study”, Tribology International 135, pp. 65 – 74, 2019. DOI: 10.1016/j.triboint.2019.01.041
- [8] Li, X., Seignez, E., Lambert, A., & Loonis, P. “Real-time driver drowsiness estimation by multi-source information fusion with Dempster–Shafer theory”, Transactions of the Institute of Measurement and Control 36 (7), pp. 906 – 915, 2014. DOI: 10.1177/0142331213508804
- [9] Zhong-Hui, H., Yun-zu, C., Yuan-Gui, L., & Xiao-ming, X. “Data fusion for fault diagnosis using multi-class Support Vector Machines”, Journal of Zhejiang University-SCIENCE A 6 (10), pp. 1030 – 1039, 2005. DOI: 10.1631/jzus.2005.A1030
- [10] Liu, C., Sun, J., Wang, F., Ning, S., & Xu, G. “Bayesian network method for fault diagnosis of civil aircraft environment control system”, Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234 (5), pp. 662 – 674, 2020. DOI: 10.1177%2F0959651819884747
- [11] Wang, X., Zhang, Y., Wang, L., Wang, J., & Lu, J. “Maintenance grouping optimization with system multi-level information based on BN lifetime prediction model”, Journal of Manufacturing Systems 50, pp. 201 – 211, 2019. DOI: 10.1016/j.jmsy.2019.01.002
- [12] Sun, J., Zuo, H., Liang, K., & Chen, Z. “Bayesian Network-Based Multiple Sources Information Fusion Mechanism for Gas Path Analysis”, Journal of Propulsion and Power 32(3), pp. 611 – 619, 2016. DOI: 10.2514/1.B35658
- [13] Raheja, D., Llinas, J., Nagi, R., & Romanowski, C. “Data fusion/data mining-based architecture for condition-based maintenance”, International Journal of Production Research 44(14), pp. 2869 – 2887, 2006. DOI: 10.1080/00207540600654509
- [14] Niu, G., Lee, S. S., Yang, B. S., & Lee, S. J. “Decision fusion system for fault diagnosis of elevator traction machine”, Journal of Mechanical Science and Technology 22(1), pp. 85 – 95, 2008. DOI: 10.1007/s12206-007-1010-0
- [15] Li, X., Chen, J., Wang, Z., Jia, X., & Peng, X. “A non-destructive fault diagnosis method for a diaphragm compressor in the hydrogen refueling station”, international journal of hydrogen energy 44 (44), pp. 24301 – 24311, 2019. DOI: 10.1016/j.ijhydene.2019.07.147
- [16] Potočnik, P., Govekar, E. “Semi-supervised vibration-based classification and condition monitoring of compressors”, Mechanical Systems and Signal Processing 93, pp. 51 – 65, 2017. DOI: 10.1016/j.ymssp.2017.01.048
- [17] Mobley, R. K. “An introduction to predictive maintenance”, Elsevier, 2002.
- [18] Riaz, S., Elahi, H., Javaid, K., & Shahzad, T. “Vibration feature extraction and analysis for fault diagnosis of rotating machinery-a literature survey”, Asia Pacific Journal of Multidisciplinary Research 5(1), pp. 103 – 110, 2017.
- [19] Saadat, B., Hafaifa, A., Belhadef, R., Kouzou, A. “Fuzzy Prognosis System for Decision Making to Vibrations Monitoring in Gas Turbine”, Strojnícky časopis – Journal of Mechanical Engineering 71 (2), pp. 239 – 256, 2021. DOI: 10.2478/scjme-2021-0033
- [20] Zaza, G., Hammou, A. D., Benchatti, A., Saiah, H. “Fault detection method on a compressor rotor using the phase variation of the vibration signal”, International Journal of Engineering 30 (8), pp. 1176 – 118, 2017.
- [21] Yan, S. F., Ma, B., Zheng, C. S., & Chen, M. “Weighted evidential fusion method for fault diagnosis of mechanical transmission based on oil analysis data”, International Journal of Automotive Technology 20 (5), pp. 989 – 996, 2019. DOI: 10.1007/s12239-019-0093-9
- [22] Jin, S., Liu, Y., & Lin, Z. “A Bayesian network approach for fixture fault diagnosis in launch of the assembly process”, International Journal of Production Research 50 (23), pp. 6655 – 6666, 2012. DOI: 10.1080/00207543.2011.611543
- [23] Feng, G., Zhang, J. D., & Liao, S. S. “A novel method for combining Bayesian networks, theoretical analysis, and its applications”, Pattern Recognition 47 (5), pp. 2057 – 2069, 2014. DOI: 10.1016/j.patcog.2013.12.005
- [24] Liu, Z., Liu, Y., Shan, H., Cai, B., & Huang, Q. “A fault diagnosis methodology for gear pump based on EEMD and Bayesian network”, PloS one, 10(5), e0125703, 2015. https://doi.org/10.1371/journal.pone.0125703
- [25] Yang, W. S., Su, Y. X., & Chen, Y. P. “Air compressor fault diagnosis based on lifting wavelet transform and probabilistic neural network”, In IOP Conference Series: Materials Science and Engineering,657 (1), p. 012053, October 2019.
- [26] Golmoradi, M., Ebrahimi, E., & Javidan, M. “Compressor fault diagnosis based on SVM and GA”, Vibroengineering Procedia 12, pp. 49 – 53, 2017. DOI: 10.21595/vp.2017.18392
- [27] Nail, B., Kouzou, A., Hafaifa, A., Hadroug, H., & Puig Cayuela, V. “A Robust fault diagnosis and forecasting approach based on Kalman filter and Interval Type-2 Fuzzy Logic for efficiency improvement of centrifugal gas compressor system”, Diagnostyka, 20 (2), pp. 57 – 75, 2019. DOI: 10.29354/diag/108613