References
- [1] Jahromi, A. T., Er, M. J, Li, X., Lim, B. S. “Sequential fuzzy clustering based dynamic fuzzy neural network for fault diagnosis and prognosis”, Neurocomputing 196, pp. 31 – 41, 2016.
- [2] Djaidir, B., Hafaifa, A., Abdallah, K. “Faults detection in gas turbine rotor using vibration analysis under varying conditions” Journal of Theoretical and Applied Mechanics 55 (2), pp. 393 – 406, 2017.
- [3] Chen, B., Matthews, P. C., Tavner, P. J. “Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS”, Expert Systems with Applications 40 (17), pp. 6863 – 6876, 2013.
- [4] Chen, B., Matthews, P. C., Tavner, P. J. “Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS”, Expert Systems with Applications 40 (17), pp. 6863 – 6876, 2013.
- [5] Fleischer, Ch., Waag, W., Bai, Z. “Dirk Uwe Sauer, On-line self-learning time forward voltage prognosis for lithium-ion batteries using adaptive neuro-fuzzy inference system”, Journal of Power Sources 243, pp. 728 – 749, 2013.
- [6] Kwon, D., Yoon, J. “A model-based prognostic approach to predict interconnect failure using impedance analysis”, Journal of Mechanical Science and Technology 30 (10), pp. 4447 – 4452, 2016.
- [7] Li, D., Wang, W., Ismail, F. “Enhanced fuzzy-filtered neural networks for material fatigue prognosis”, Applied Soft Computing 13 (1), pp. 283 – 291, 2013.
- [8] Ewins D. J. “Control of vibration and resonance in aero engines and rotating machinery – An overview”, International Journal of Pressure Vessels and Piping 87 (09), pp. 504 – 510, 2010.
- [9] Sica, F. C., Guimarães, F. G., Duarte, R. O., Agnaldo, J. R. R. “A cognitive system for fault prognosis in power transformers”, Electric Power Systems Research 127, pp. 109 – 117, 2015.
- [10] Tsai, G. Ch. “Rotating vibration behavior of the turbine blades with different groups of blades”, Journal of Sound and Vibration 271 (3-5, 6), pp. 547 – 575, 2004.
- [11] Hafaifa, A., Mouloud, G., Daoudi, A. “Vibration supervision in gas turbine based on parity space approach to increasing efficiency”, Journal of Vibration and Control 21, pp. 1622 – 1632, 2015.
- [12] Kanoglu, M., Dincer, I., Rosen, M. A. “Understanding energy and exergy efficiencies for improved energy management in power plants”, Energy Policy 35 (1), pp. 3967 – 3978, 2007.
- [13] Kessentini, S., Choura, S., Najar, F., Franchek, M. A. “Modeling and dynamics of a horizontal axis wind turbine”, Journal of Vibration and Control 16 (13), pp. 2001 – 2021, 2010.
- [14] Žmindák, M. “Dynamic and sensitivity analysis general non-conservative asymmetric mechanical systems”, Strojnícky časopis – Journal of Mechanical Engineering 68 (2), pp. 105 – 124, 2018. DOI: 10.2478/scjme-2018-0021
- [15] Benrahmoune, M., Hafaifa, A., Mouloud, G. “Vibration modeling improves pipeline performance, costs”, Oil & Gas Journal 113 (03), pp. 98 – 100, 2015.
- [16] Benrahmoune M., Hafaifa, A., Guemana, M., Chen, X. “Detection and modeling vibrational behavior of a gas turbine based on dynamic neural networks approach”, Strojnícky časopis – Journal of Mechanical Engineering 68 (3), pp. 143 – 166, 2018. DOI: 10.2478/scjme-2018-0032
- [17] Hadroug, N., Hafaifa, A., Kouzou, A. “Mouloud Guemana and Ahmed Chaibet, Control of the speed and exhaust gas temperature in gas turbine using adaptive neuro-fuzzy inference system”, Journal of Automation & Systems Engineering, 10 (3), pp. 158 – 167, 2016.
- [18] Nadji, H., Ahmed, H., Bachir, A., Abdelhamid, I., Chen, X. “Fuzzy diagnostic strategy implementation for gas turbine vibrations faults detection: Towards a characterization of symptom–fault correlations”, Journal of Vibration Engineering & Technologies, 2021. DOI: 10.1007/s42417-021-00373-z
- [19] Nadji, H., Ahmed, H., Kouzou, A., Ahmed, Ch. “Dynamic model linearization of two shafts gas turbine via their input / output data around the equilibrium points”, Energy Elsevier 120, pp. 488 – 497, 2017.
- [20] Okipnyi, I., Poberezhny, L., Zapukhliak, V., Hrytsanchuk, A,, Poberezhna, L., Stanetsky, A., Kravchenko, V., Rybitskyi, I. “Impact of long-term operation on the reliability and durability of transit gas pipelines”, Strojnícky časopis – Journal of Mechanical Engineering 70 (1), pp. 115 – 126, 2020. DOI: 10.2478/scjme-2020-0011
- [21] Qinming, L., Ming, D., Wenyuan, L., Xiuli, G., Yupeng, L. “A novel method using adaptive hidden semi-Markov model for multi-sensor monitoring equipment health prognosis”, Mechanical Systems and Signal Processing 64–65, pp. 217 – 232, 2015.
- [22] Stetter, R., Witczak, P., Spindler, C., Hertel, J., Witczak, M. “Intelligent Systems for the Prognosis of Energy Consumption in Manufacturing and Assembly”, Procedia CIRP 33, pp. 370 – 375, 2015.
- [23] Owsiński, R., Niesłony, A. “Identification of fatigue cracks on the basis of measurable changes in system dynamics”, Strojnícky časopis – Journal of Mechanical Engineering 67 (2), pp. 77 – 84, 2017. DOI: 10.1515/scjme-2017-0020
- [24] Smoczek J., Szpytko, J. “Evolutionary algorithm-based design of a fuzzy TBF predictive model and TSK fuzzy anti-sway crane control system”, Engineering Applications of Artificial Intelligence 28, pp. 190 – 200, 2014.
- [25] Hua, S., Yi, Z., Maolin, Z., Jingjing, S. “Fault prognosis approach for satellite attitude control system based on T-S Model and time series analysis”, IFAC Proceedings 46 (20), pp. 456 – 461, 2013.
- [26] Welte, T. M., Wang, K. “Models for lifetime estimation: an overview with focus on applications to wind turbines”, Advances in Manufacturing 2 (1), pp. 79 – 87, 2014.
- [27] Traore M., Chammas, A., Duviella, E. “Supervision and prognosis architecture based on dynamical classification method for the predictive maintenance of dynamical evolving systems”, Reliability Engineering & System Safety 136, pp. 120 – 131, 2015.
- [28] Tůma, J., Šuránek, P.], Žiaran, S. “All-Pass filters in the systems of active vibration control of weakly-damped systems”, Strojnícky časopis – Journal of Mechanical Engineering 68 (4), pp. 65 – 74, 2018. DOI: 10.2478/scjme-2018-0048
- [29] Leturiondo, U., Salgado, O., Ciani, L., Galar, D., Catelani, M. “Architecture for hybrid modelling and its application to diagnosis and prognosis with missing data”, Measurement 108, pp. 152 – 162, 2017.
- [30] Tran, V. T., Yang, B-S., Tan, A. Ch. Ch. “Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems”, Expert Systems with Applications 36 (5), pp. 9378 – 9387, 2009.
- [31] Wang, W. Q., Golnaraghi, M. F., Fathy, I., “Prognosis of machine health condition using neuro-fuzzy systems”, Mechanical Systems and Signal Processing 18 (4), pp. 813 – 831, 2004.
- [32] Chen, X., Yu, J., Tang, D., Wang, Y. “A Novel PF-LSSVR-based framework for failure prognosis of nonlinear systems with time-varying parameters”, Chinese Journal of Aeronautics 25 (5), pp. 715 – 724, 2012.
- [33] Tian, X., Cao, Y. P., Chen, S. “Process fault prognosis using a fuzzy-adaptive unscented Kalman predictor”, International Journal of Adaptive Control and Signal Processing,, 25 (9), pp. 813 – 830, 2011.
- [34] Lei, Y., He, Z., Zi, Y., Hu, Q. “Fault diagnosis of rotating machinery based on a new hybrid clustering algorithm”, The International Journal of Advanced Manufacturing Technology 35 (9-10), pp. 968 – 977, 2008.
- [35] Pan, Y., Er, M. J., Li, X., Yu, H., Gouriveau, H. “Machine health condition prediction via online dynamic fuzzy neural networks”, Engineering Applications of Artificial Intelligence 35, pp. 105 – 113, 2014.
- [36] Wu, X., Hong G. S., Wong W.S. “Prognosis of the probability of failure in tool condition monitoring application-a time series based approach”, The International Journal of Advanced Manufacturing Technology 76 (1 – 4), pp. 513 – 521, 2015.