Abstract
In aviation industry, prediction of engine failure time is very important. The engine is the main power device for aircraft flight and the consequences if they fail could be extremely serious. Predicting engine failure is a key task in airline repair control. This study predicts engine failure using principal component analysis (PCA). Variable replacement is a well-known technique to improve PCA prediction performance, hence we propose a hybrid method incorporating PCA, categorical regression tree and a back propagation network (PCA–CART–BPN) to engine failure time prediction. The proposed PCA–CART–BPN method effectively improved prediction accuracy and achieved satisfactory results. The proposed method first applies PCA analysis and classification and regression tree (CART) clustering, and then inputs these outcomes into a back propagation network (BPN) for training and testing. Experimental and analytical comparisons show that the proposed PCA–CART–BPN method provided significantly improved prediction accuracy to the PCA–CART method. The proposed PCA–CART–BPN method improved the performance by 2.9% (MAPE) and 39% (RMSE) over PCA–CART alone.
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References
Abdelrahman WG, Al-Garni AZ, Al-Wadiee W (2012) Application of back propagation neural network algorithms on modeling failure of B-737 bleed air system valves in desert conditions. Appl Mech Mater 225:505–510
Altay A, Ozkan O, Kayakutlu G (2014) Prediction of aircraft failure times using artificial neural networks and genetic algorithms. J Aircr 51(1):47–53
Bevilacqua M, Braglia M, Montanari R (2003) The classification and regression tree approach to pump failure rate analysis. Reliab Eng Syst Saf 79(1):59–67
Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth and brooks. Cole Advanced Books and Software Google Scholar, Monterey
Chang PT, Lin KP, Pai PF (2004) Hybrid learning fuzzy neural models in forecasting engine system reliability. In: Proceeding of the fifth Asia Pacific industrial engineering and management systems conference. Citeseer, pp 2361–2366
Chang SN, Leng MY, Wu HW, Thompson J (2016) Aircraft ice accretion prediction using neural network and wavelet packet transform. Aircr Eng Aerosp Technol 88(1):128–136
Chen T (2011) Forecasting job cycle time in a wafer fabrication factory by the FPCA-FBPN approach. Int Rev Comput Softw 6:1050
Chen T (2013) A systematic cycle time reduction procedure for enhancing the competitiveness and sustainability of a semiconductor manufacturer. Sustainability 5(11):4637–4652
Chen T (2014) The symmetric-partitioning and incremental-relearning classification and back-propagation-network tree approach for cycle time estimation in wafer fabrication. Symmetry 6(2):409–426
Chen T, Romanowski R (2013) Precise and accurate job cycle time forecasting in a wafer fabrication factory with a fuzzy data mining approach. Math Probl Eng 2013:14
Chen T, Wang YC (2014) Enhancing the effectiveness of cycle time estimation in wafer fabrication-efficient methodology and managerial implications. Sustainability 6(8):5107–5128
Clark G (1950) The organization of behavior: a neuropsychological theory. DO Hebb. Wiley, New York, p 335 (1949)
Frohlich H, Chapelle O, Scholkopf B (2003) Feature selection for support vector machines by means of genetic algorithm. In: Tools with artificial intelligence 2003. Proceedings 15th IEEE international conference on IEEE, pp 142–148
Fun MH, Hagan MT (1996) Levenberg–Marquardt training for modular networks. IEEE Int Conf 1:468–473
Guangbin Y, Ding G, Lin L, **ngfu Z, Yang Z (2014) Aircraft engine fuel flow prediction using process neural network. Complex Syst 7:3
Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5:6
Jayawardena AW (2013) Environmental and hydrological systems modelling. CRC Press, Boca Raton
Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI-95), vol 14. Morgan Kaufmann, San Francisco, CA, pp 1137–1145
Kontrec NZ, Milovanović GV, Panić SR, Milošević H (2015) A reliability-based approach to nonrepairable spare part forecasting in aircraft maintenance system. Math Probl Eng 2015:7
Krogh A, Vedelsby J (1995) Neural network ensembles, cross validation, and active learning. Adv Neural Inf Process Syst 7:231–238
McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133
Nguyen AT, Han JH, Nguyen AT (2017) Application of artificial neural networks to predict dynamic responses of wing structures due to atmospheric turbulence. Int J Aeronaut Space Sci 18(3):474–484
Pearson K (1901) On lines and planes of closest fit to systems of points in space. Phil Mag 2(6):559–572
Ren C, Wu R, Cai Z, Zhang DP (2016) Research on the principle component analysis on selecting the military vehicle equipment supplier. J Residuals Sci Technol 13(7)
Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386
Secco NR, Mattos de BS (2017) Artificial neural networks to predict aerodynamic coefficients of transport airplanes. Aircr Eng Aerospace Technol 89(2):211–230
Sikorska J, Hodkiewicz M, Ma L (2011) Prognostic modelling options for remaining useful life estimation by industry. Mech Syst Signal Process 25(5):1803–1836
Werbos P (1974) Beyond regression: new fools for prediction and analysis in the behavioral sciences. PhD thesis. Harvard University
Widrow B, Hoff ME (1960) Adaptive switching circuits. Stanford University Stanford Electronics Labs
**a Q, Tian YD, Zhu XW, Xu DW, Zhang J (2015) Structural damage detection by principle component analysis of long-gauge dynamic strains. Struct Eng Mech 54(2):379–392
Xu K, **e M, Tang LC, Ho S (2003) Application of neural networks in forecasting engine systems reliability. Appl Soft Comput 2(4):255–268
Yusof MFM, Nizwan CKE, Ong SA, Ridzuan MQM (2017) Clustering of frequency spectrums from different bearing fault using principle component analysis. In: MATEC Web of Conferences, vol 90, p 1006
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Wang, YC. Prediction of engine failure time using principal component analysis, categorical regression tree, and back propagation network. J Ambient Intell Human Comput 14, 14531–14539 (2023). https://doi.org/10.1007/s12652-018-0997-7
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DOI: https://doi.org/10.1007/s12652-018-0997-7