The prediction of several failure modes of an industrial equipment requires the development of prediction systems with several interdependent parameters. The integration of future maintenance actions with this type of prediction system is a major asset for maintenance decision making. This is even more relevant in the event that after having predicted the future occurrence of several failure modes, the maintenance department does not have the necessary resources to correct all the predicted failure modes at once. In this case it becomes necessary to know how much longer the equipment will work if future partial maintenance actions that do not correct all failure modes are implemented. It is to contribute to the resolution of this problem that we propose an architecture integrating the future maintenance actions to the prediction of several interdependent parameters. This architecture is based on the association of Proportional Integral Derivative regulators to Neuro-Fuzzy systems taking into account the four previous instants to predict the next instant. An application is made with accuracies of the order of 70% for the prediction of the phenomena of fouling of the coolers and of the order of 90% for the prediction of the phenomena of clogging of the filters of the ATLAS COPCO compressor, this with Central Processing Unit values not exceeding one minute.
future maintenance actions, prediction, ATLAS COPCO compressor, Multi-outputs Adaptive Neuro-Fuzzy System, Proportional Integral Derivative
Adeline, R. Gouriveau, R. and Zerhouni, N. (2008). Pronos-tic de défaillances: Maîtrise de l'erreur de prédiction. LISMMA, CRAN, ENSTIB. 7ème Conférence Internatio-nale de Mobilisation et Simulation, MOSIM’08. Paris, France. 1 (sur CD ROM), 10 p.
Atlas Copco, (2014). Instruction manual, N ° 2922 1815 03, consulted on December 08, 2017 on the site www.atlascopco.com.
Atsalakis, G. S., Atsalaki, I. G., and Zopounidis, C. (2018). Forecasting the success of a new tourism service by a neuro-fuzzy technique. European Journal of Operational Research, 268(2), 716-727.
Auand, S. M. and Priyanka C. P. (2016). A Comparative Analysis of Fault prediction and Speed Control of Induction Motor using ANN and ANFIS, International Advanced Research Journal in Science, Engineering and Technology. National Conference on Emerging Trends in Engineering and Technology (NCETET’16). Lourdes Matha College of Science & Technology, Thiruvananthapuram. Vol. 3, Special Issue 3.
Bayatzadeh, F. Z., Ghadimi F. and Fattahi H. (2016). Use of artificial intelligence techniques to predict distribution of heavy metals in groundwater of Lakan lead-zinc mine in Iran. Journal of Mining & Environment, Vol.8, No.1, 35-48. DOI: 10.22044/jme. 592, 2017.
Bouzidi, Z., Terrissa, L. S., Zerhouni, N. and Ayad S. (2020). QoS of cloud prognostic system: application to aircraft engines fleet. European J. Industrial Engineering, Vol. 14, No. 1, https://doi.org/10.1504/EJIE.2020.105080.
Cao, Y., Babanezhad, M., Rezakazemi, M., and Shirazian, S. (2020). Prediction of fluid pattern in a shear flow on intelligent neural nodes using ANFIS and LBM. Neural Computing and Applications, 32(17), 13313-13321.
Chai, T. & Draxler, R. R. (2014). Rootmean square error (RMSE) ormean absolute error (MAE) – arguments against avoiding RMSE in the literature. Geosci Model Dev, 7(3), 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014.
Daher,A., Hoblos, G., Khalil, and M., Chetouani, M. (2020). New prognosis approach for preventive and predictive maintenance — Application to a distillation column. Chemical Engineering Research and Design, Volume 153, Pages 162-174, ISSN 0263-8762, https://doi.org/10.1016/j.cherd.2019.10.029
Dragomir, O. E. Dragomir, F. Stefan, V. and Minca, E. (2015). Adaptive neuro-fuzzy inference systems as a strategy for predicting and controling the energy produced from re-newable sources. Energies, 8, pp. 13047–13061. [Google Scholar] [CrossRef].
Dragomir, O. E. V. (2008). Contribution to the Prognosis of Failures by Neuro-Fuzzy Network: Control of the Prediction Error. Automatic/Robotics. University of Franche Comté, France. French.
El Adraoui, I., Gziri, H., & Mousrij, A. (2020a). Diagnostic and Prognostic Model for a System for Guiding a Microwave Oven Subjected to Degradation. International Journal of Advanced Science and Technology, Vol 29 - No. (3), pp. 14503 14519.http://sersc.org/journals/index.php/IJAST/article/view/31935.
El Adraoui, I., Gziri, H., and Mousrij, A. (2020b). Prognosis of a Degradable Hydraulic System: Application on a Centrifugal Pump. International Journal of Prognostics and Health Management, ISSN 2153-2648.
Elasha, F., Shanbr, S., Li, X., and Mba, D. (2019). Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning. Sensors, 19, 3092. https://doi.org/10.3390/s19143092
Elbaz, K., Shen, S. L., Sun, W. J., Yin, Z. Y., and Zhou, A. (2020). Prediction model of shield performance during tunneling via incorporating improved particle swarm optimization into ANFIS. IEEE Access, 8, 39659-39671.
Gouriveau, R., & Medjaher, K. (2011). Prognostics. Part: Industrial Prognostic-An Overview. Maintenance Modelling and Applications. 10-30.
Hanachi, H., Jie, L. I. U., and Mechefske, C. (2018). Multi-mode diagnosis of a gas turbine engine using an adaptive neuro-fuzzy system. Chinese Journal of Aeronautics, 31(1), 1-9.
Hanachi, H., Liu, J., Banerjee, A., and Chen, Y. (2016). Prediction of Compressor Fouling Rate Under Time Varying Operating Conditions. Proceedings of the ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. Volume 6: Ceramics; Controls, Diagnostics and Instrumentation; Education; Manufacturing Materials and Metallurgy. Seoul, South Korea. V006T05A003. ASME. https://doi.org/10.1115/GT2016-56242.
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. Int J Forecast, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001.
Jang, J. S. R. & Sun, C.T. (1995). Neuro-fuzzy model-ing and control. Proceedings of the IEEE 83 (3), pp. 378-406.
Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Trans. Syst., Man, and Cy-bern, 23, pp. 665-685.
Jang, J. S. R., Suni, C. T., and Mizutani, E. (1997). Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. New York: Prentice Hall.
Jeong, Y., Son, S., Cho, S.K., Baik, S., and Lee, J.I. (2020). Evaluation of supercritical CO2 compressor off-design performance prediction methods, Energy, Volume 213, 119071, ISSN 0360-5442, https://doi.org/10.1016/j.energy.2020.119071.
Jombo, G., Pecinka, J., Sampath, S., and Mba, D. (2018). "Influence of Fouling on Compressor Dynamics: Experimental and Modeling Approach." ASME. J. Eng. Gas Turbines Power. 140(3): 032603. https://doi.org/10.1115/1.4037913
Kar S., Das S., and Ghosh P. K., (2014), Applications of neuro fuzzy systems: A brief review and future outline, Applied Soft Computing, Volume 15, Pages 243-259, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2013.10.014.
Karahoca A., Karahoca D. (2011). GSM churn management by using fuzzy c-means clustering and adaptive neuro fuzzy inference system, Expert Systems with Applications, Volume 38, Issue 3, Pages 1814-1822, ISSN0957-4174, https://doi.org/10.1016/j.eswa.2010.07.110.
Kordestani, M., Samadi, M. F., and Saif, M. (2020). A New Hybrid Fault Prognosis Method for MFS Systems Based on Distributed Neural Networks and Recursive Bayesian Algorithm. IEEE Systems Journal, vol. 14, no. 4, pp. 5407-5416, doi: 10.1109/JSYST.2020.2986162.
Lemaitre González, E. (2019). Definition of a prognosis function for multi-component technical systems taking into account the uncertainties from the prognoses of their components (Doctoral dissertation).
Lobo, M. & Guntur, R. D. (2018). Spearman’s rank correlation analysis on public perception toward health partnership projects between Indonesia and Australia in East Nusa Tenggara Province, J. Phys.: Conf. Ser. 1116 022020.
Martínez-Soto R., Castillo O., and Castro J.R. (2014) Genetic Algorithm Optimization for Type-2 Non-singleton Fuzzy Logic Controllers. In: Castillo O., Melin P., Pedrycz W., Kacprzyk J. (eds) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-319-05170-3_1.
Motahari-Nezhad, M. & Jafari, S.M. (2020). ANFIS system for prognosis of dynamometer high-speed ball bearing based on frequency domain acoustic emission signals. Measurement, 166, 108154, https://doi.org/10.1016/j.measurement.2020.108154.
Naphon, P., Arisariyawong, T., Wiriyasart, S., & Srichat, A. (2020). ANFIS for analysis friction factor and Nusselt number of pulsating nanofluids flow in the fluted tube under magnetic field. Case Studies in Thermal Engineering, 18, 100605.
Perstedt, I. & Tuhkanen, S. (2017). Industrial transformation of the compressor industry.
Sammouri, W. (2014). Data mining of temporal sequences for the prediction of infrequent failure events: application on oating train data for predictive maintenance. Signal and Image processing. Université Paris-Est, English.
Soualhi, A., Medjaher, K., Celrc, G., and Razik, H. (2020). Prediction of bearing failures by the analysis of the time series. Mechanical Systems and Signal Processing, Volume 139, 106607, ISSN 0888-3270, https://doi.org/10.1016/j.ymssp.2019.106607.
Soualhi, A., Razik, H., and Clerc, G. (2019). Data Driven Methods for the Prediction of Failures. IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), pp. 474-480, doi: 10.1109/DEMPED.2019.8864877.
Soualhi, M., Nguyen, K.T.P., Soualhi, A., Medjaher, K., and Hemsas, K.E. (2019). Health Monitoring of Bearing and Gear Faults by Using a New Health Indicator Extracted From Current Signals. Measurement, Volume 141, Pages 37-51, ISSN 0263-2241, doi: https://doi.org/10.1016/j.measurement.2019.03.065
Sparthan, T., Nzie, W., Soh Fotsing, B., Beda, T. and Garro, O. (2020). A Valorized Scheme for Failure
Suganya, R., & Shanthi, R. (2012). Fuzzy c-means algorithm-a review. International Journal of Scientific and Research Publications, 2(11), 1.
Syahputra, R. (2016). Application of neuro-fuzzy method for prediction of vehicle fuel consumption. Journal of Theoretical and Applied Information Technology (JATIT), 86(1), pp. 138-149.
Tjahe, A. V., Mtopi Fotso, B. E., Djanna, F. K., and Fogue M. (2017a). Proposition of multi-ANFIS Architecture Mounted in Series for the Multi-Parameters Prediction. In-ternational Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, Vol. 6, Issue 07, pp. 646-652.
Tjahe, A. V., Mtopi Fotso, B. E., Djanna, F. K., and Fogue M. (2017b). Proposition of a MANFIS-PID Hybrid System for the Prediction of Several Interdependent Parameters. International Journal of Engineering Research & Technolo-gy (IJERT), ISSN: 2278-0181, Vol. 6, Issue 07, p. 653-661.
Wang, H., Hong, R., Chen, J., and Tang, M. (2015). Intel-ligent health evaluation method of slewing bearing adopting multiple types of signals from monitoring system. International Journal of Engineering (IJE), TRANSACTIONS A: Basics Vol. 28, No. 4, pp. 573-582.
Wang, J., Zhang, L., Duan, L., and Gao, R., X. (2017). A new paradigm of cloud-based predictive maintenance for intelligent manufacturing. Journal of Intelligent Manufacturing, 28(5), 1125-1137.
Wong Y. J., Arumugasamy S. K., Chung C. H., Selvarajoo A., and Sethu V. (2020). Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu (II) adsorption from aqueous solution using biochar derived from rambutan (Nephelium lappaceum) peel. Springer Nature Switzerland AG, https://doi.org/10.1007/s10661-020-08268-4.
Zagorowska, M., Spüntrup, F. S., Ditlefsen, A. M., Imsland, L., Lunde, E., and Thornhill, N. F. (2020). Adaptive detection and prediction of performance degradation in off-shore turbomachinery. Applied Energy, 268, 114934.
Zemouri R. (2003), Recurrent Radial Basis Function network for Time-Series Prediction, Eng Appl of Artif Intell, 16, 453-463.
Zemouri R., Gouriveau R. and Patic P. C. (2010). Combining a recurrent neural network and a PID controller for prognostic purpose: A way to improve the accuracy of predictions, Wseas Transactions On Systems And Control, 5, 353-371.
Zhang, L., Lin, J., Liu, B., Zhang, Z., Yan, X., & Wei, M. (2019). A review on deep learning applications in prognostics and health management. IEEE Access, 7, 162415-162438.
Zhuang, X., Yu, T., Sun, Z., and Song, K. (2021). Wear prediction of a mechanism with multiple joints based on ANFIS. Engineering Failure Analysis, 119, 104958.
Zid, K., Ahmed, M.B. and Turki, M. (2018) Modeling of Flank Wear Using ANFIS. Proceedings of the 4th International Conference on Engineering & MIS, Istanbul, Article No. 47.