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Multi Sources Information Fusion Based on Bayesian Network Method to Improve the Fault Prediction of Centrifugal Compressor

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[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.2017.10.01110.1016/j.apacoust.2017.10.011 Search in Google Scholar

[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.2010.557940610.1109/ICACTE.2010.5579406 Search in Google Scholar

[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-2021-004010.2478/scjme-2021-0040 Search in Google Scholar

[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_3710.1007/978-3-319-95711-1_37 Search in Google Scholar

[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.2018.06.05110.1016/j.energy.2018.06.051 Search in Google Scholar

[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-F10.1016/0263-2241(91)90012-F Search in Google Scholar

[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.2019.01.04110.1016/j.triboint.2019.01.041 Search in Google Scholar

[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: 10.1177/014233121350880410.1177/0142331213508804 Search in Google Scholar

[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.A103010.1631/jzus.2005.A1030 Search in Google Scholar

[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%2F095965181988474710.1177/0959651819884747 Search in Google Scholar

[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.2019.01.00210.1016/j.jmsy.2019.01.002 Search in Google Scholar

[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.B3565810.2514/1.B35658 Search in Google Scholar

[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: 10.1080/0020754060065450910.1080/00207540600654509 Search in Google Scholar

[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/s12206-007-1010-010.1007/s12206-007-1010-0 Search in Google Scholar

[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.2019.07.14710.1016/j.ijhydene.2019.07.147 Search in Google Scholar

[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.2017.01.04810.1016/j.ymssp.2017.01.048 Search in Google Scholar

[17] Mobley, R. K. “An introduction to predictive maintenance”, Elsevier, 2002.10.1016/B978-075067531-4/50006-3 Search in Google Scholar

[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. Search in Google Scholar

[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-2021-003310.2478/scjme-2021-0033 Search in Google Scholar

[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. Search in Google Scholar

[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/s12239-019-0093-910.1007/s12239-019-0093-9 Search in Google Scholar

[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: 10.1080/00207543.2011.61154310.1080/00207543.2011.611543 Search in Google Scholar

[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.2013.12.00510.1016/j.patcog.2013.12.005 Search in Google Scholar

[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.012570310.1371/journal.pone.0125703441856625938760 Search in Google Scholar

[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. Search in Google Scholar

[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.2017.1839210.21595/vp.2017.18392 Search in Google Scholar

[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/10861310.29354/diag/108613 Search in Google Scholar

eISSN:
2450-5471
Lingua:
Inglese
Frequenza di pubblicazione:
2 volte all'anno
Argomenti della rivista:
Engineering, Mechanical Engineering, Fundamentals of Mechanical Engineering, Mechanics