Implementation of a Diagnostic Approach Based on Vibration Analysis: Case Study of a Hydroelectric Group

Implementation of a Diagnostic Approach Based on Vibration Analysis: Case Study of a Hydroelectric Group

© 2021 by IJETT Journal
Volume-69 Issue-9
Year of Publication : 2021
Authors : Imad El Adraoui, Mohammed Bouaicha, Hassan Gziri, Mourad Zegrari
DOI :  10.14445/22315381/IJETT-V69I9P213

How to Cite?

Imad El Adraoui, Mohammed Bouaicha, Hassan Gziri, Mourad Zegrari, "Implementation of a Diagnostic Approach Based on Vibration Analysis: Case Study of a Hydroelectric Group," International Journal of Engineering Trends and Technology, vol. 69, no. 9, pp. 97-106, 2021. Crossref,

This paper deals with the implementation of a diagnostic approach on a hydroelectric group; the technique implemented is based on the analysis of the vibrations acquired. This analysis makes it possible to monitor and control the state of the running system during operation in a relevant way. To do this, tests are carried out to visualize the behavior of the hydroelectric group for different cases in order to judge its state of health. Each test is generated by several measurements, and the latter is taken by vibration sensors at various predefined points. The hydroelectric group is considered to be in poor operating condition if it does not meet the requirement of ISO 10816-5. Consequently, an intervention by the maintainer must be taken into account.

Approach, Diagnosis, Degradation, Vibration analysis, Hydroelectric group.

[1] I. El Adraoui, H. Gziri, A. Mousrij. Integration of a Prognosis Model of a Rotating Microwave Oven Guidance System Subject to Linear Degradation. Lecture Notes in Mechanical Engineering, (2021) 446–458.
[2] T. Fakhfakh, F. Chaari, and M. Haddar, Numerical and experimental analysis of a gear system with teeth defects. International Journal of Advanced Manufacturing Technology, 25 (5) (2005) 542-550.
[3] Z. Li, X.Yan, C. Yuan, Z. Peng, L. Li, Virtual prototype and experimental research on gear multi-fault diagnosis using waveletautoregressive model and principal component analysis method. Mechanical Systems and Signal Processing, 25 (2011) 2589– 2607.
[4] El. Semma, A. Mousrij, H. Gziri, « Development of a conditional maintenance implementation approach based on vibration analysis». MOSIM 2014, 10th Francophone Conference on Modeling, Optimization and Simulation, Nancy, France, (2014).
[5] I. El Adraoui, H. Gziri, A. Mousrij,, Diagnosis and Prognosis Based On the Vibration Analysis of Rotating Machines: Study of a Vibration Test Bench». International Journal of Advanced Science and Technology, 29(3) (2020) 14199 - 14211. Retrieved from
[6] Y-K. Akilu, and C. Ruifeng, Towards Developing an Automated Faults Characterisation Framework for Rotating Machines. Part 1: Rotor-Related Faults,. Energies, 13 (1394) (2020), doi:10.3390/en13061394.
[7] L. Qianjun, M. Guijun, and C. Cheng, Data Fusion Generative Adversarial Network for Multi-Class Imbalanced Fault Diagnosis of Rotating Machinery. IEEE Access, 8 (2020) DOI: 10.1109/ACCESS.2020.2986356.
[8] A. Yunusa-Kaltungo, and J. Sinha, Generic vibration-based faults identification approach for identical rotating machines installed on different foundations. In VIRM 11 - Vibrations in Rotating Machinery, (2016) 499-510.
[9] L. Jing, T. Wang, M. Zhao, and P. Wang, An Adaptive Multi- Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox. Sensors, 17 (2017) 414, doi:10.3390/s17020414.
[10] I. El Adraoui, H. Gziri, and A. Mousrij, Prognosis of a Degradable Hydraulic System: Application on a Centrifugal Pump. International Journal of Prognostics and Health Management, 11(2) 013 (2020) 11.
[11] S. Hao, F.X. Ge, Y. Li, and J. Jiang, Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks. Measurement, 159-107802, (2020),
[12] P. Nectoux, R. Gouriveau, K. Medjaher, E. Ramasso, B. Chebel- Morello, et al., PRONOSTIA: An experimental platform for bearings accelerated degradation tests. IEEE International Conference on Prognostics and Health Management, PHM’12., Denver, Colorado, United States, (2012) 1-8. hal-00719503.
[13] B. Kahramano?lu, E. D. Ülker, S. Ülker, Integrating Support Vector Machine (SVM) Technique and Contact Imaging for Fast Estimation of the Leaf Chlorophyll Contents of Strawberry Plants. International Journal of Engineering Trends and Technology 69(3) (2021) 23-28.
[14] T. Aggab., Prognosis of complex systems by the joint use of hidden Markov model and observer. University of Orleans, 2016. French. . .