Prognosis of a Degradable Hydraulic System Application on a Centrifugal Pump



Published Mar 24, 2021
Imad El Adraoui Hassan Gziri Ahmed Mousrij


This article proposes a preliminary diagnostic/prognostic method for the identification of a critical system, undergoing a continuous evolutionary degradation, in a production area, and the determination of the component responsible for its degradation, called the failing element. Using for this, a model based on learning  by multilayer perception (MLP). The purpose of this paper is to provide a modeling approach that makes it possible to determine the level of degradation reached by the system at any given point of time, in a precise way. Thus, the horizon of the failure will be produced with a minimum error compared to the discrete jump model used in the literature. The proposed approach consists of using a neural network with fewer layers and optimal computing time. We performed data learning (tests) in order to illustrate a regression of good correlation of these data (tests) on a centrifugal pump with satisfactory performance parameters and compared it with other commonly used methods.

Abstract 340 | PDF Downloads 273



diagnostics, prognostics, degradation, centrifugal pump, MLP

Abdenour, S., Bilal, E., Yasmine, H., Kamal, M., Guy, C., Razik, H., & François, G. (2019). PHM Survey: Implementation of Diagnostic Methods for Monitoring Industrial Systems, International Journal of Prognostics and Health Management. HAL Id: hal-02111790.
Adamt, T. (1976). Turbopumps, Eyrolles, Paris. Adam, S.B. (September 6, 2012), Neural networks.
Aggab, T. (2016). Prognosis of complex systems by the joint use of hidden Markov model and observer. PhD thesis, ffNNT: 2016 ORLE2051, University of Orleans. HAL Id: tel-01674253.
Aggab, T., Vrignat, P., Avila, M., & Kratz, F. (Mar 2015). Estimation of the level of degradation by a multi-flow hidden Markov model. QUALITA’ 2015, Nancy, France. HAL Id: hal-01149798.
Christer, A.H. and Wang, W. (1992). A model of condition monitoring of a production plant. International Journal of Production Research, 30(9) :2199–2211.
De Gooijer, J. G. & Hyndman, R. J. (2006). 25 years of time series forecasting. International journal of forecasting,22(3):pp.443-473.
Dragomir, O. E. (2008). Contribution to the prognosis of failures by neuro-fuzzy network: control of the prediction error. PhD thesis, Franche-Comte University. HAL Id: tel-00362509. https://tel.archives-
El Adraoui, I., Gziri, H., & Mousrij., A. (2020). Diagnostic and Prognostic Model for a System for Guiding a MicrowaveOvenSubjectedto Degradation. International Journal of Advanced Science and Technology, Vol 29 - No. (3), pp. 14503– 14519. view/31935.
El Adraoui, I., Gziri, H., & Mousrij., A. (2020). Diagnosis and Prognosis Based On the Vibration Analysis of Rotating Machines: Study of a Vibration Test Bench. International Journal of Advanced Science and Technology, Vol 29 – No. (3), pp. 14199 - 14211. 1871.
ENSPM Industry Training. (2005). IFP Training (Risks and Precautions related to PUMP Equipment).
Fatima, M., & Hamid, S. (2009). Comparison of RBF network classification methods, MLP et RVFLNN1. Damascus University Journal, Vol, (25)-No. (2).
Jang, J. S. R., Sun, C. T., & Mizutani, E. (OCTOBER 1997). Neuro-Fuzzy and Soft Computing—A Computational Approach to Learning and Machine Intelligence - (Englewood Cliffs, NJ: Prentice-Hall, 1997). Reviewed by Yu-Chi Ho. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 42, NO. 10.
Koivo, H. (1994). Artificial neural networks in fault diagnosis and control, Control engineering practice, vol. 2, pp. 89-101.
Krishnakumar, P., Rameshkumar, K., & Ramachandran, K. I. (2018). Feature level fusion of vibration and acoustic emission signals in tool condition monitoring using machine learning classifiers. International Journal of Prognostics and Health Management, vol. 9, Issue 1.
Letot, C. (2013). Forecast maintenance of industrial equipment based on modeling, estimation and simulation of degradation laws. PhD thesis, Polytechnic Faculty of Mons.
McClelland, J. L., Rumelhart, D. E., & Group, P. R. (1986). Parallel distributed processing. Explorations in the microstructure of cognition, vol. 2.
Msaaf, M., & Belmajdoub, F. (2015). The application of "feedforward" neural networks in static diagnostics. Xth International Conference: Integrated Design and Production, Tangier, Morocco.
Mustafaraja, G., Lowryb, G., & Chena, J. (June 2011). Prediction of room temperature and relative humidity by autoregressive linear and nonlinear neural network models for an open office. Energy and Buildings, Volume 43, Issue 6, Pages 1452-1460.
Nachlas, J. A. (December 1, 2016). Reliability engineering probabilistic models and maintenance methods. CRC press. 378 Pages.
Nguyen, D. N. (2015). Contribution to probabilistic approaches for the prognosis and maintenance of Controlled systems. PhD thesis, University of Technology of Troyes. Press. ID: 2015TROY0010.
Nakagawa, T. (2007). Shock and damage models in reliability theory. Springer Science & Business Media. DOI: 10.1007/978-1-84628-442-7.
Park, J. W., Harley, R., & Venayagamoorthy, G. (2002). Comparison of MLP and RBF neural networks using deviation signals for on-line identification of a synchronous generator. IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309). DOI: 10.1109/PESW.2002.984998.
Saxena, A., Celaya, J., Balaban, E., Saha, S., Saha, B., & Schwarbacher, M. (2008). Metrics for evaluating performance of prognostics techniques. International Conference on Prognostics and Health Management, Denver, CO, USA. DOI: 10.1109/PHM.2008.4711436.
Saxena, A., Celaya, J., Saha, S., Saha, B., & Goebel, K. (2009). On applying the prognostic performance metrics. Proceedings of the 1th Annual Conference of the Prognostics and Health Management Society, San Diego, CA. DOI: 10.1109/PHM.2008.4711436.
Soualhi, A. (2013). From diagnosis to prognosis of electrical drive breakdowns. PhD thesis, Claude Bernard-Lyon I University.
Takaaki, T., Yukihiro, T., & Takehisa, Y. (2020). Scalable Change Analysis and Representation Using Characteristic Function, International Journal of Prognostics and Health Management. Issue:1.
Tiddens, W.W., Braaksma, A.J.J., & Tinga, T. (2018). Selecting Suitable Candidates for Predictive Maintenance. International Journal of Prognostics and Health Management. ID: 67230356.
Tomohiro, T., & Michio, S. (Jan.-Feb. 1985). Fuzzy identification of systems and its applications to modeling and control”, IEEE Transactions on Systems, Man, and Cybernetics. Volume: SMC-15, Issue: 1.
Vrignat, P., Avila, M., Duculty, F., & Kratz, F. (2015). Failure Event Prediction Using Hidden Markov Model Approaches. IEEE Transactions on Reliability, 64(3): pp: 1038-1048.
Welte, T. (2008). Deterioration and maintenance models for components in hydropower plants. PhD thesis, Department of Production and Quality Engineering, Norwegian University of Science and Technology, Trondheim.
Zhang, Y. (2019). Patient-Specific Readmission Prediction and Intervention for Health Care. International Journal of Prognostics and Health Management.
Technical Papers