[1] E. khavasi, S. Nasiri, Analysis and Simulation of Surge phenomena in the axial Compressor of GE-frame 6 gas Turbine, Amirkabir Journal of Mechanical Engineering, online published (2019) (in persian).
[2] R. Jiang, C. Huang, Predicting distribution of time to degradation limit using a weighted approach, Journal of Mechanical Science and Technology, 32(11) (2018) 5133-5138 (in Persian) .
[3] R. Bannazadeh, R. M, A. M., Failure Analysis of a Gas Turbine Blade Made of Inconel 738LC Super Alloy, Amirkabir Journal of Mechanical Engineering, 50 (2018) 103-112.
[4] D. Kwon, J. Yoon, A model-based prognostic approach to predict interconnect failure using impedance analysis, Journal of Mechanical Science and Technology, 30(10) (2016) 4447-4452.
[5] D. An, J.H. Choi, K. N, Prediction of remaining useful life under different conditions using accelerated life testing data, Journal of Mechanical Science and Technology, 32 (2018) 2497-2507.
[6] F.O. Heimes, Recurrent neural networks for remaining useful life estimation, international conference on prognostics and health management (IEEE), (2008) 1-6.
[7] J. Lee, F. Wu, W. Zhao, M. Ghaffari, L. Liao, D. Siegel, Prognostics and health management design for rotary machinery systems-Reviews, methodology and applications, Mechanical systems and signal processing, 42 (2014) 20.
[8] S.M. Raghavan, A. Palatel, J. Simon, Health Assessment of Gas Turbine Compressor Using Process History Based Modelling Approach, ASME 2015 Gas Turbine India Conference (pp. V001T08A001-V001T08A001). American Society of Mechanical Engineers., (2015).
[9] M. Garsia A. Munoz, A. Sola, Hybrid model-based fault detection and diagnosis for the axial flow compressor of a combined-cycle power plant, Journal of Engineering for Gas Turbines and Power, 135 (2013).
[10] R. Sekhon, Real Time Prognostic Strategies Application to Gas Turbines, Clemson university Technical Report (2007).
[11] P. Baraldi, F. Cadini, F. Mangili, E. Zio, Model-based and data-driven prognostics under different available information, Probabilistic Engineering Mechanics, 32 (2013) 66-79.
[12] Y.G. Li, P. Nilkitsaranont, Gas turbine performance prognostic for condition-based maintenance, Applied energy, 86 (2009) 2152-2161.
[13] F. Lu, J. Wu, J. Huang, X. Qiu, Aircraft engine degradation prognostics based on logistic regression and novel OS-ELM algorithm, Aerospace Science and Technology, (2019) 661-671.
[14] D. An, N.H. Kim, J.H. Choi, Statistical aspects in neural network for the purpose of prognostics, . Journal of Mechanical Science and Technology, 29 (2015) 1369-1375.
[15] W. Zhao, A Probabilistic Approach for Prognostics of Complex Rotary Machinery Systems, Doctoral dissertation, University of Cincinnati, (2015).
[16] J. Son, S. Zhou, C. Sankavaram, X. Du, Y. Zhang, Remaining useful life prediction based on noisy condition monitoring signals using constrained Kalman filter, Reliability Engineering & System Safety, 152 (2016) 38-50.
[17] O.N. Diallo, A data analytics approach to gas turbine prognostics and health management, Doctoral dissertation, Georgia Institute of Technology, (2010).
[18] M. Daigle, S. Sankararaman, Predicting remaining driving time and distance of a planetary rover under uncertainty, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2 (2016).
[19] W. Caesarendra, W. , A. , B.S. Yang, Combination of probability approach and support vector machine towards machine health prognostics, Probabilistic Engineering Mechanics, 26 (2011) 165-173.
[20] H.Z. Huang, H.K. Wang, Y.F. Li, L. Zhang, Z. Liu, Support vector machine based estimation of remaining useful life: current research status and future trends, Journal of Mechanical Science and Technology, (2015) 151-163.
[21] F. Lu, J. Wu, J. Huang, X. Qiu, Aircraft engine degradation prognostics based on logistic regression and novel OS-ELM algorithm, Aerospace Science and Technology, 84 (2019) 661-671.
[22] D. Simon, A comparison of filtering approaches for aircraft engine health estimation, Aerospace Science and Technology, (2008) 276-284.
[23] K.T. McClintic, Feature prediction and tracking for monitoring the condition of complex mechanical systems, Doctoral dissertation, Pennsylvania State University, (1998).
[24] D.C. Swanson, A general prognostic tracking algorithm for predictive maintenance, IEEE Aerospace Conference Proceedings, (2001) 2971-2977.
[25] F. Lu, H. Ju, J. Huang, An improved extended Kalman filter with inequality constraints for gas turbine engine health monitoring, Aerospace Science and Technology,, (58) (2016) 36-47.
[26] C. Ding, J. Xu, L. Xu, ISHM-based intelligent fusion prognostics for space avionics, Aerospace Science and Technology,, 29 (2013) 200-205.
[27] Goebel K, Saha B, S. A.. A comparison of three data-driven techniques for prognostics. In62nd meeting of the society for machinery failure prevention technology, (2008) 119-131.
[28] J. Xu, Y. Wang, L. Xu, PHM-oriented integrated fusion prognostics for aircraft engines based on sensor data, IEEE Sensors Journal, 14 (2013) 1124-1132.
[29] R. Salehi, A. Alasty, G.R. Vossoughi, Sliding Mode Observers to Detect and Isolate Faults in a Turbocharged Gasoline Engine, SAE International Journal of Engines, 8 (2015) 399-410.
[30] N. Puggina, M. Venturini, Development of a Statistical Methodology for Gas Turbine Prognostics, ASME 2011 Turbo Expo, Turbine Technical Conference and Exposition, 4 (2011) 981-992.
[31] M. Venturini, D. Therkorn, Application of a statistical methodology for gas turbine degradation prognostics to alstom field data, Journal of Engineering for Gas Turbines and Power,, 135 (2013).
[32] M. Venturini, N. Puggina, Prediction reliability of a statistical methodology for gas turbine prognostics, Journal of Engineering for Gas Turbines and Power, 134 (2012).
[33] Musavi A, R. H., Failure analysis of compressor blades in a gas turbine, International Power System Conference, 22 (2009) (in Persian).
[34] C.M. Holcomb, Diagnostics and Control of Gas Turbines Through System Identification, Doctoral dissertation, UC San Diego, (2015).
[35] P. Wang, G. Vachtsevanos, Fault prognostics using dynamic wavelet neural networks, AI EDAM, 15 (2001) 349-365.
[36] R. Yan, R.X. Gao, X. Chen, Wavelets for fault diagnosis of rotary machines: A review with applications, Signal processing, 96 (2014) 1-15.
[37] R. Mao, H. Zhu, L. Zhang, A. Chen, A new method to assist small data set neural network learning, Sixth International Conference on Intelligent Systems Design and Applications 1(2006) 17-22.
[38] R. Moghaddass, M.J. Zuo, An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process, Reliability Engineering & System Safety, (2014) 92-104.
[39] Y. Xiang, Y. Liu, Application of inverse first-order reliability method for probabilistic fatigue life prediction, Probabilistic Engineering Mechanics, 26 148-156.
[40] M. Gholamrezaei, K. Ghorbanian, Application of integrated fuzzy logic and neural networks to the performance prediction of axial compressors, Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 229 (2015) 928-947.
[41] D. Zhou, H. Zhang, S. Weng, A novel prognostic model of performance degradation trend for power machinery maintenance, Energy, 78 (2014) 740-746.
[42] A. Saxena, K. Goebel, D. Simon, Damage propagation modeling for aircraft engine run-to-failure simulation, international conference on prognostics and health management (IEEE), (2008 ) 1-9.
[43] E. Ramasso, M. Rombaut, N. Zerhouni, Joint prediction of observations and states in time-series: a partially supervised prognostics approach based on belief functions and KNN, networks, (2013).
[44] R. Khelif, S. Malinowski, B. Chebel-Morello, N. Zerhouni, RUL prediction based on a new similarity-instance based approach, 23rd International Symposium on Industrial Electronics (ISIE), (2014) 2463-2468.
[45] E. Ramasso, Investigating computational geometry for failure prognostics, International Journal of Prognostics and Health Management,, 5 (2014).
[46]A, Mahmoodina. M Durali, Saadat M, A Data Driven Prognostics Method for Gas Turbines with Limited Information Using an Age Based Clustering Algorithm, 20th International Conference on Gas Turbines,, (2018) (in Persian).
[47] M. Tahan, E. Tsoutsanis, M. Muhammad, Z.A. Karim, Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review, Applied energy, 198 (2017) 122-144.
[48] M. Makvandi H , Moradi.S., Crack identification in postbuckled plates using differential quadrature element method and sequential quadratic programming, Amirkabir Journal of Mechanical Engineering, online published (2018).
[49] S.R. Prasad, A.S. Sekhar, Life estimation of shafts using vibration based fatigue analysis, Journal of Mechanical Science and Technology, 32 (2018) 4071-4078.
[50] C. Fisher, N.C. Baines, . Multi-sensor condition monitoring systems for gas turbines, Journal of Condition Monitoring, (1988) 57-68.
[51] D. Muir, B. Taylor, Oil debris monitoring for aeroderivative gas turbine, ASME Power Division (Publication) PWR,, (1997).547-553.
[52] M. Behzad, A. Ebrahimi, M. Heydari, M. Asadi, Experimental investigation on the fault diagnosis of permanent magnet DC electromotors, Insight, 55 (2013) 1-8.
[53] M. Behzad, M.R. Hoseini, H. Salmasi, M. Asayesh, Fault Diagnosis In Two Industrial Applications By Vibration Analysis, Proceeding of IMEC 2004, (2004) 591-603.
[54] M. Behzad, A.R. Bastami, M. Maassoumian, Fault diagnosis of a centrifugal pump by vibration analysis, ASME 7th Biennial Conference on Engineering Systems Design and Analysis (2004) 221-226.
[55] M. Behzad, M. Asoyesh, Steam turbine coupling misalignment detection by vibrational analysis, Journal of Electrical Science and Technology, 13 (2001) 47-53.
[56] A.H. Zamanian, A. Ohadi, 2011, Gear fault diagnosis based on Gaussian correlation of vibrations signals and wavelet coefficients, Applied Soft Computing, 11 (2011) 4807-4819.
[57] N Mahmoodi, Lari H, Using vibrating properties of gas turbines to prevent failure, 15th International Power System Conference, (1999).
[58] E. Halim, Fault Detection And Diagnosis Of Rotating Machineries, Doctoral dissertation, Alberta university, (2009).
[59] M. Rezaei, B. .M., M. , Moradi, H. , H. Haddadpour, Modal-based damage identification for the nonlinear model of modern wind turbine blade, Renewable energy,, 94 (2016) 391-409.
[60] E. Mohammadi, M. Montazeri-Gh, Simulation of full and part-load performance deterioration of industrial two-shaft gas turbine, Journal of Engineering for Gas Turbines and Power,, 136 (2014).
[61] B. Zhou, K. Bhimavarapu, . Effect of Condition Monitoring on Risk Mitigation for Steam Turbines in the Forest Products Industry, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 3 (2017).
[62] A Mahmoodian,. M Durali, Saadat M, Investigating Different Structures for Mapping Sensor Information of a Complex Mechanical System to Its Health Status, Proceedings of the 26th ISME Conference, (2018) 110-114 (in persian)
[63] M Kamari,G Payeganeh., Implementation of Neuro– Fuzzy and Multi-Layer Perceptron System Intelligent Techniques for Main Fault Diagnosis of Rotating Machinery, Amirkabir Journal of Mechanical Engineering, 45 (2013) 105:118
[64] E. Ramasso, A. Saxena, . Review and analysis of algorithmic approaches developed for prognostics on CMAPSS dataset, International Journal of Prognostics and Health Management,, (2014).
[65] T. Wang, J. Yu, Siegel, J. D. and Lee, A similarity-based prognostics approach for remaining useful life estimation of engineered systems, In 2008 international conference on prognostics and health management, (2008) 1-6.
[66] K. Javed, R. Gouriveau, N. Zerhouni, Novel failure prognostics approach with dynamic thresholds for machine degradation, In IECON 2013-39th Annual Conference of the IEEE Industrial Electronics Society, (2013) 4404-4409.