Document Type : Research Paper


Department of Mechanical Engineering, Higher Institute of Technological Studies of Sfax, ISET de Sfax, Tunisia.



The evolution of the means aimed at improving the availability of strategic equipment requires a credible level of maintenance with the development of original monitoring techniques such as vibration diagnostics. The vibration monitoring of strategic equipment through a vibration diagnosis requires mainly the identification of vibrational images of the different types of damage. However, one of the important vibration problems is essentially due to the phenomenon of imbalance. This phenomenon corresponds to an imbalance of the rotor due to the offset between the axis of inertia and the axis of rotation, which causes significant and cyclical vibrations. The aim of this study is to analyze the vibratory behavior of a screw compressor to improve its reliability and consequently its availability. To identify the imbalance fault, two sets of vibration measurements on April and January were fundamentally examined at the compressor level. Fourier transform based on spectral analysis was used to create a vibration detection approach with vibration signals. The comparison of the results obtained with that of the simulation resulting from the model of dynamic behavior of the compressor is conclusive.


Main Subjects

[1]   Morel, J. (1992). Vibration monitoring and predictive maintenance. Ed. Techniques Engineer. (In French).
[2]   Meslameni, W. (2020). Diagnosis of rotating machines by vibration analysis. (In French).
[3]   Gangsar, P., & Tiwari, R. (2020). Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review. Mechanical systems and signal processing144, 106908.
[4] Ikpe, A. E., Etuk, E. M., & Adoh, A. U. (2019). Modelling and analysis of 2-stage planetary gear train for modular horizontal wind turbine application. Journal of applied research on industrial engineering6(4), 268-282. Doi: 10.22105/jarie.2020.213154.1114
[5] Ikpe, A. E., Iluobe, I. C., & Imonitie, D. I. E. (2020). Modelling and simulation of high pressure fogging air intake cooling unit of Omotosho phase II gas turbine power plant. Journal of applied research on industrial engineering7(2), 121-136. Doi: 10.22105/jarie.2020.216680.1129
[6] Meslameni, W., & Salema, C. B. (2018). Detection of electro compressor bearing defects by vibration monitoring Detection of electro-compressor bearing defects by vibration monitoring. International journal of applied research and technology, 1, 8-12. (In French).
[7] Augeix, D. (2001). Vibration analysis of rotating machines. Ed. Techniques Engineer. (In French).
[8] Abou elanouar, B., Elamrani, M., Elkihel, B., & Delaunois, F. (2017, May). Study and development of a predictive maintenance system based on vibratory analysis for the monitoring of rotating machines. Paper presented at international conference on civil engineering and materials (ICCEM'2017). Al Hoceima, Morocco.
[9] Mubaraali, L., Kuppuswamy, N., & Muthukumar, R. (2020). Intelligent fault diagnosis in microprocessor systems for vibration analysis in roller bearings in whirlpool turbine generators real time processor applications. Microprocessors and microsystems76, 103079.
[10] Debbah, Y., Cherfia, A., & Saadi, A. (2015). Application of the neural network method for the prediction of vibrations induced by combined faults (misalignment and unbalance). International conference of mechanics (ICM'15-2) (320-327). Constantine, Algeria. (In French).
[11] González-Muñiz, A., Díaz, I., & Cuadrado, A. A. (2020). DCNN for condition monitoring and fault detection in rotating machines and its contribution to the understanding of machine nature. Heliyon6(2), e03395.
[12] Bogard, F., Debray, K., Guo, Y. Q., & Pavan, A. (2002). Numerical methodology to easily detect defects in revolving machines by vibration analysis. ELSEVIER, Mécanique and industries3, 79-87.
[13] Zhao, B., & Yuan, Q. (2021). Improved generative adversarial network for vibration-based fault diagnosis with imbalanced data. Measurement169, 108522.
[14] Zhang, M., Wang, T., Tang, T., Benbouzid, M., & Diallo, D. (2017). An imbalance fault detection method based on data normalization and EMD for marine current turbines. ISA transactions68, 302-312.
[15] Wei, J., Huang, H., Yao, L., Hu, Y., Fan, Q., & Huang, D. (2020). New imbalanced fault diagnosis framework based on Cluster-MWMOTE and MFO-optimized LS-SVM using limited and complex bearing data. Engineering applications of artificial intelligence96, 103966.
[16] Kaidi, I., & Kabouche, A. (2018). Analysis of unbalance defect in a hydrodynamic bearing.  Summary: Journal of Science and Technology, 36, 87-102. (In French).
[17] Yamamoto, G. K., da Costa, C., & da Silva Sousa, J. S. (2016). A smart experimental setup for vibration measurement and imbalance fault detection in rotating machinery. Case studies in mechanical systems and signal processing4, 8-18.
[18] Salem, S. B., Bacha, K., & Chaari, A. (2012). Support vector machine based decision for mechanical fault condition monitoring in induction motor using an advanced Hilbert-Park transform. ISA transactions51(5), 566-572.
[19] Xu, B., Sun, L., Xu, L., & Xu, G. (2013). Improvement of the Hilbert method via ESPRIT for detecting rotor fault in induction motors at low slip. IEEE transactions on energy conversion28(1), 225-233.Doi: 10.1109/TEC.2012.2236557
[20] Asad, B., Vaimann, T., Kallaste, A., Rassõlkin, A., & Belahcen, A. (2019, June). Hilbert transform, an effective replacement of park's vector modulus for the detection of rotor faults. 2019 electric power quality and supply reliability conference (pq) and 2019 symposium on electrical engineering and mechatronics (SEEM) (pp. 1-4). Kärdla, Estonia: IEEE Doi: 10.1109/PQ.2019.8818227
[21] Cabal-Yepez, E., Garcia-Ramirez, A. G., Romero-Troncoso, R. J., Garcia-Perez, A., & Osornio-Rios, R. A. (2012). Reconfigurable monitoring system for time-frequency analysis on industrial equipment through STFT and DWT. IEEE transactions on industrial informatics9(2), 760-771.Doi: 10.1109/TII.2012.2221131
[22] Climente-Alarcon, V. I. C. E. N. T. E., Antonino-Daviu, J. A., Riera-Guasp, M., Puche-Panadero, R., & Escobar, L. (2012). Application of the Wigner–Ville distribution for the detection of rotor asymmetries and eccentricity through high-order harmonics. Electric power systems research91, 28-36.
[23] Ma, C. Y., Wu, L., Sun, M., & Yuan, Q. (2021). Time-Frequency analysis and application of a vibration signal of tunnel excavation blasting based on CEEMD-MPE-HT. Shock and vibration.
[24] Kumar, S., Lokesha, M., Kumar, M. K., & Ramachandra, C. G. (2021, January). Fault diagnosis in belt transmission using wavelet enveloped spectrum. IOP conference series: materials science and engineering (Vol. 1013, No. 1, p. 012025). IOP Publishing. Doi:10.1088/1757-899X/1013/1/012025
[25] Hafaifa, A., Guemana, M., & Daoudi, A. (2015). Vibrations supervision in gas turbine based on parity space approach to increasing efficiency. Journal of vibration and control21(8), 1622-1632.
[26] Mohammadi, E., Fadaeinedjad, R., & Moschopoulos, G. (2019, October). Performance investigation of a stall-regulated wind turbine considering rotor imbalance faults. IECON 2019-45th annual conference of the IEEE industrial electronics society (IECON 2019), (Vol. 1, pp. 2469-2474). Lisbon, Portugal: IEEE. Doi: 10.1109/IECON.2019.8927386
[27] Hafaifa, A., Guemana, M., & Daoudi, A. (2013). Fault detection and isolation in industrial systems based on spectral analysis diagnosis. Journal of intelligent control and automation, 4(1), 36-41.