Search for Author, Title, Keyword
ANN-based failure modeling of classes of aircraft engine components using radial basis functions
More details
Hide details
Department of Aerospace Engineering King Fahd University of Petroleum and Minerals Dhahran, 31261, Saudi Arabia
Publication date: 2019-06-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2019;21(2):311–317
The objective of this research is to present a model to predict failure of two categories of critical aircraft engine components; nonrotating components such as valves and gearboxes, and rotating components such as engine turbines. The work utilizes Weibull regression and artificial neural networks employing Back Propagation (BP) as well as Radial Basis Functions (RBF). The model utilizes training failure data collected from operators of turboprop aircraft working in harsh desert conditions, where sand erosion is a detrimental factor in reducing turbine life. Accordingly, the model is more suited for accurate prediction of life of critical components of such engines. The algorithm, which uses Radial Basis Function (RBF) NN, uses a closest point specifier. The activation is based on the deviation of the earlier prototype from the input vector. Two earlier models are used for comparison purposes; namely Weibull regression modeling and Feed-Forward BP network. Comparison results show that the failure times represented by RBF are in better compromise with actual failure data than both earlier modeling methods. Moreover, the technique has comparatively higher efficiency as the neuron’s number in each layer of ANN is reduced, to decrease computation time, with minimum effect on the accuracy of results.
1 Abdelrahman W G, Al-Garni A Z, Al-Wadiee W. Application of back propagation neural network algorithms on modeling failure of B-737 bleed air system valves in desert conditions. Applied Mechanics and Materials 2012; 225: 505-510,
Al-Garni A Z, Ahmed S, Siddiqui M. Modeling failure rate for Fokker F-27 tires using neural network. Transactions – Japan Society for Aeronautical and Space Sciences 1998; 41: 29-37.
Al-Garni A Z, Jamal A, Ahmad A, Al-Garni A, Tozan M. Failure-rate prediction for De Havilland Dash-8 tires employing neural network technique. AIAA Journal of Aircraft 2006; 43(2): 537-543,
Al-Garni A Z, Tozan M, Al-Garni A, Jamal A. Failure forecasting of aircraft air conditioning/cooling pack with field data. Journal of Aircraft 2007; 44(3): 996-1002,
Al-Qutub A, Al-Garni A Z. Comparison between neural network and Weibull models for failure of Boeing 737 engines. Transactions of the Japan Society for Aeronautical and Space Sciences 1999; 42(137):128-134.
Al-Wadiee W. Back propagation neural network algorithms on modeling failure of B-737 bleed air system valves in desert conditions. MS Thesis: King Fahd University of Petroleum and Minerals, 2011.
Bin G, Gao J, Li X, Dhillon B. Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network. Mechanical Systems and Signal Processing 2012; 27: 696-711,
Broomhead D, Lowe D. Multivariable function interpolation and adaptive networks. Computer Systems 1988; 2: 321–355.
Hopfield J. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences 1982; 79(8): 2554-2558,
Kutsurelis J. Forecasting financial markets using neural networks: An analysis of methods and accuracy. PhD dissertation: Naval Postgraduate School, 1998.
Lin X S, Li B W, Yang X. Engine components fault diagnosis using an improved method of deep belief networks. 7th International Conference on Mechanical and Aerospace Engineering (ICMAE) 2016; 18-20 July 2016,
Nieto P G, García-Gonzalo E, Lasheras F S, de Cos Juez, F J. Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability. Reliability Engineering & System Safety 2015; 138: 219-231,
Parker D. Learning logic. Technical Report TR-87. Cambridge, MA: Center for Computational Research in Economics and Management Science MIT, 1985.
Paul S, Kapoor K, Jasani, D, Dudhwewala R, Gowda V B, Nair T R. Application of artificial neural networks in aircraft maintenance, repair and overhaul solutions. arXiv preprint arXiv:1001.3741, 2010.
Qattan N A. Reliability analysis of C-130 turboprop engine components using artificial neural network. PhD dissertation: King Fahd University of Petroleum and Minerals, 2013.
Sheikh A K, Al-Garni, A Z, Affan Badar M. Reliability analysis of aeroplane tyres. International Journal of Quality & Reliability Management 1996; 13(8): 28-38,
Tian Z. An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring. Journal of Intelligent Manufacturing 2012; 23(2): 227-237,
Tozan M, Al-Garni A Z, Jamal A. Failure Distribution Modeling for Planned Replacement of Aircraft Auxiliary Power Unit Oil Pumps. Maintenance Journal 2006; 19(1): 60-69.
Vanini Z S, Khorasani K, Meskin N. Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks and multiple model approach. Information Sciences 2014; 259: 234-251,
Wang Z, Wang P. A new approach for reliability analysis with time-variant performance characteristics. Reliability Engineering & System Safety 2013; 115:70-81,
Zaretsky E Y. Fatigue criterion to system design, life, and reliability. Journal of Propulsion and Power 1987; 3(1): 76-83,
An artificial neural network supported performance degradation modeling for electro-hydrostatic actuator
Songlin Nie, Jianhang Gao, Zhonghai Ma, Fanglong Yin, Hui Ji
Journal of the Brazilian Society of Mechanical Sciences and Engineering
Predicting thrust of aircraft using artificial neural networks
Dalkiran Yildirim, Mustafa Toraman
Aircraft Engineering and Aerospace Technology
Artificial intelligence-based hybrid forecasting models for manufacturing systems
Maria Rosienkiewicz
Eksploatacja i Niezawodnosc - Maintenance and Reliability
A review of aircraft subsonic and supersonic combustors
Gubran Abdulrahman, Naef Qasem, Binash Imteyaz, Ayman Abdallah, Mohamed Habib
Aerospace Science and Technology
Identification of crashworthiness indicators of column energy absorbers with triggers in the form of cylindrical embossing on the lateral edges using artificial neural networks
Mirosław Ferdynus, Jakub Gajewski
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Research on reliability of centrifugal compressor unit based on dynamic Bayesian network of fault tree mapping
Gao Yuan, Zhang Liang, Zhou Jiawei, Wei Bojia, Yan Zhongchao
Journal of Mechanical Science and Technology