Design an algorithm for damage detection of a liquid propellant engine based on neural network in order to classification and identification of quantity and place of damage

Document Type : Research Paper

Authors

1 PhD student / Faculty of Engineering and New Technologies, Shahid-Beheshti University, Tehran

2 Graduate student / Mechanical Engineering Department, Guilan University, Rasht

3 PhD / Aerospace Engineering Department, K. N. Toosi University of Technology, Tehran

Abstract

 
The aim of this paper is to design an Algorithm for damage detection of the open cycle liquid propellant engine which is based on artificial neural networks in combination with stochastic analysis. Damage is simulated as cavitation in pumps (oxidizer or fuel pump) and fouling in some path of engine. The key stone of the method is feed-forward multi-layer neural network with back propagation algorithm. This network uses output signals of unhealthy system to detect place and quantity of damage. It is impossible to obtain appropriate training set for real engine, so stochastic analysis using mathematical model is carried out and dynamic simulation is made to get training set virtually. Result of dynamic simulation of engine is validated with experimental result. In this plan, percentage of variation of output signals of engine such as output pressure of subsystem and revolution of turbine, considered as best input data for neural network. This data is obtained from output parameters of simulated unhealthy engine. Finally, this damage detection approach was carried out using laboratory hot test.

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References

[1] L. H.Yam, Y. J. Yan, J. S. Jiang, Vibration-based damage detection for composite structures using wavelet transform and neural network identification, Composite Structures, Vol. 60, Issue 4, pp. 403-412, 2003.
[2] Y. F. Hua, L. H. Bing, T. G. Jin, Application of neural network ensemble for structural damage detection, Journal of Jilin University (Engineering and Technology Edition), 2007.
[3] S. Rajakarunakaran, P. Venkumar, D. Devaraj, Surya Prakasa Rao K., Artificial neural network approach for fault detection in rotary system, Artificial neural network approach for fault detection in rotary, Vol. 8, Issue 1, pp. 740-748, 2008.
[4] J. D. Wu, J. J. Chan, Faulted gear identification of a rotating machinery based on wavelet transform and artificial neural network, Expert Systems with Applications, Vol. 36, Issue 5, pp. 8862-8875, 2009.
[5] J. D. Wu, C. K. Huang, Y. W. Chang, Y. J. Shiao, Fault diagnosis for internal combustion engines using intake manifold pressure and artificial neural network, Expert Systems with Applications, Vol. 37, Issue 2, pp. 949-958, 2010.
[6] H. A. Talebi, A Recurrent Neural-Network-Based Sensor and Actuator Fault Detection and Isolation for Nonlinear Systems with Application to the Satellite's Attitude Control Subsystem, IEEE Transactions on Neural Networks, Vol. 20, Issue 1, pp. 45-60, 2009.
[7] N. Dervilis, R. J. Barthorpe, I. Antoniadou, W. J. Staszewski, K. Worden, Damage detection in carbon composite material typical of wind turbine blades using auto-associative neural networks. (Proceedings Paper), Health Monitoring of Structural and Biological Systems, Vol. 8348, 2012.
[8] M. Claudia, W. A. Maul, The application of neural network to the SSME startup transient, Sverdrup Technology, Inc., NASA contractor report 187138, June 1991.
[9] M. Claudia, J. F. Zakrajesk., The Rocket engine failure detection using system identification techniques, Sverdrup Technology, Inc., NASA contractor report 185259, AIAA-90-1993, June 1990.
[10] J. Wu, Liquid-propellant rocket engines health-monitoring-a survey, Acta Astronautica, 56, pp. 347-356, 2005.
[11] Z. Feng, Q. Wang, Research on health evaluation system of liquid-propellant rocket engine ground-testing bed based on fuzzy theory, Acta Astronautica, Vol. 61, pp. 840-853, 2007.
[12] S. khodadadian, R.Farrokhi, D. Ramesh, Construction of a Neural Network Based Troubleshooting Model for a liquid propellant  Engine with a Defective Data Acquisition System, The 12th international conference of Iranian aerospace society, Tehran, Iran, Amirkabir university, AERO2013-17498, February 20–22, 2013.
[13] S. khodadadian, R.Farrokhi, D. Ramesh, Damage Detection of a Liquid propellant Engine Using An Intelligent Neural Network with Dynamic Simulation Based on Mathematical Analysis, Second Conference of Iranian Aerospace Propulsion society, Tehran. Iran, Tarbiat Modares university, pp. 132-134, November 20-21, 2013.
[14] D. Ramesh, S. Alimohammadi, Simulation of Cavitation Process in Oxidizer Pump of a Liquid Rocket Engine, 45th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, 2009.
[15] R. Farrokhi, D. Ramesh, Nonlinear Dynamic Simulation of a Liquid Propellant Engine with Four Chumbuction Chambers, Fifteenth International Conference of Mechanical Engineering, Tehran, Iran, Amirkabir university, May 15-16, 2007.
[16] Ch. Manfletti, Transient Simulation of Liquid Rocket Engines: A Step Towards A More Educated Propellant Choice Between Kerosene And Methane, Second International Conference on Green Propellants for Space Propulsion, Cagliari, Sardinia, Italy, ESA, 2004.
[17] E. Beliaev, V. Chevanov, Mathematical Modeling of Operating Process of Liquid Propellant Rocket Engines, In Russian, 1999.
[18] D. Ramesh, M. Aminpour, Nonlinear Dynamic Simulation of a Liquid Propellant Engine, The second international conference of Iranian aerospace society, 2003.
[19] V. Kurkova, Kolmogorov’s theorem and multilayer neural networks, Neural Networks, Vol. 5, No. 3, 1992,  pp. 501-506.
[20] M. Snorek, Neuralnetworksand neurocomputers, Vydavatelstvi, CVUT, Prague, Czech Republic, 2002.
[21] V. Singh, I. Gupta, H. O. Gupta, ANN-based estimator for distillation using Levenberg-Marquardt approach, Engineering Applications of Artificial Intelligence, Vol. 20, pp. 249-259, 2007.
Aerospace Knowledge and Technology Journal
Volume 7, Issue 2 - Serial Number 2
December 2018
Pages 109-119

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