Abstract
To analyze the reliability of aeroengine more accurately, based on the analysis of operation reliability and complex reliability, the deep learning method is adopted to deal with the nonlinear and time-varying problems between the state parameters and operation reliability of aeroengine, and the condition monitoring method and deep learning of aeroengine are discussed. The results show that, based on the deep learning integrated network, the remaining useful life of aeroengine is predicted, and the key parameters of aeroengine are fitted and predicted by the back propagation (BP) algorithm. The artificial neural network method is used to predict the aeroengine parameters. For the collected aeroengine monitoring parameters, the greedy layer by layer training algorithm is used to mine the relationship between the parameters, extract the evaluation features, and evaluate the performance degradation, which verify the statistical significance and robustness of the conclusions. The proposed algorithm is more accurate and robust than the results of back BP neural network and support vector machine. It can prevent the over-fitting of small samples in aeroengine condition monitoring and further improve its nonlinear processing and generalization ability.
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References
Adamović VM, Antanasijević DZ, Ristić MĐ, Perić-Grujić AA, Pocajt VV (2018) An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level. J Mater Cycles Waste 20(3):1–15. https://doi.org/10.1007/s10163-018-0741-6
BahooToroody A, De Carlo F, Paltrinieri N, Tucci M, van Gelder PHAJM (2020) Bayesian regression based condition monitoring approach for effective reliability prediction of random processes in autonomous energy supply operation. Reliab Eng Syst Saf. https://doi.org/10.1016/j.ress.2020.106966
Bektas O, Jones JA, Sankararaman S, Roychoudhury I, Goebel K (2019) A neural network filtering approach for similarity-based remaining useful life estimation. Int J Adv Manuf Technol 101(1–4):87–103. https://doi.org/10.1007/s00170-018-2874-0
Chen Y, Cai KZ, Tu ZH, Nie W, Ji T, Hu B, Chen CG, Jiang ST (2017) Prediction of benzo[a]pyrene content of smoked sausage using back-propagation artificial neural network. J Sci Food Agric 98:3022–3030. https://doi.org/10.1002/jsfa.8801
Fentaye AD, Ul-Haq Gilani SI, Baheta AT, Li YG (2019) Performance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network method. Proc Inst Mech Eng Part A J Power Energy 233(6):786–802. https://doi.org/10.1177/0957650918812510
Franceschini S, Gandola E, Martinoli M, Tancioni L, Scardi M (2018) Cascaded neural networks improving fish species prediction accuracy: the role of the biotic information. Sci Rep-UK 8(1):4581. https://doi.org/10.1038/s41598-018-22761-4
He Y, Xiu R, Lee G (2018) Research on the grey correlation analysis of the norm in choosing key factors of aero-engine design integration system. In: MATEC Web of Conferences 228. https://doi.org/10.1051/matecconf/201822803006
Huang C, Jia X, Zhang Z (2018) A modified back propagation artificial neural network model based on genetic algorithm to predict the flow behavior of 5754 aluminum alloy. Materials 11(5):855. https://doi.org/10.3390/ma11050855
Jiang Q, Gao F, Yi H, Yan X (2018) Multivariate statistical monitoring of key operation units of batch processes based on time-slice CCA. IEEE Trans Control Syst Technol. https://doi.org/10.1109/tcst.2018.2803071
Liu J, Lin L, Li Z, Guo H, Lv Y (2019) Spare aeroengine demand prediction model based on deep Croston method. J Aerosp Inf Syst. https://doi.org/10.2514/1.i010707
Lu Z, Huang B, Zhou Y (2018) Theoretical study and experimental validation on the energy dissipation mechanism of particle dampers. Struct Control Health. https://doi.org/10.1002/stc.2125
Lu GX, Liu H, Lin CH, Zhang Z, Shukla P, Zhang YK, Yao JH (2019) Improving the fretting performance of aero-engine tenon joint materials using surface strengthening. Mater Sci Technol-Lond 2:1–8. https://doi.org/10.1080/02670836.2019.1650445
Ma D, Tan W, Zhang Z, Wang X, Xia F, Hu J (2018) Recognition of leak CO2 with wavelet analysis based on correlation monitoring between CO2 and O2 in atmosphere. Process Saf Environ Prot 114:64–78. https://doi.org/10.1016/j.psep.2017.12.009
Maleki H, Sorooshian A, Goudarzi G, Baboli Z, Birgani YT, Rahmati M (2019) Air pollution prediction by using an artificial neural network model. Clean Technol Environ. https://doi.org/10.1007/s10098-019-01709-w
Ramyar S, Kianfar F (2019) Forecasting crude oil prices: a comparison between artificial neural networks and vector autoregressive models. Comput Econ. https://doi.org/10.1007/s10614-017-9764-7
Ruben GB, Ke Z, Bao H, Ma X (2017) Application and sensitivity analysis of artificial neural network for prediction of chemical oxygen demand. Water Resour Manag 32(1):1–11. https://doi.org/10.1007/s11269-017-1809-0
Shen C-W, Ho J-T, Ly PTM, Kuo T-C (2019a) Behavioural intentions of using virtual reality in learning: perspectives of acceptance of information technology and learning style. Virtual Real 23(3):313–324. https://doi.org/10.1007/s10055-018-0348-1
Shen C-W, Min C, Wang C-C (2019b) Analyzing the trend of O2O commerce by bilingual text mining on social media. Comput Human Behav 101:474–483. https://doi.org/10.1016/j.chb.2018.09.031
Singh P (2018) Indian summer monsoon rainfall (ISMR) forecasting using time series data: a fuzzy-entropy-neuro based expert system. Geosci Front. https://doi.org/10.1016/j.gsf.2017.07.011
Sousa RVD, Rodrigues AVDS, Abreu MGD, Tabile RA, Martello LS (2018) Predictive model based on artificial neural network for assessing beef cattle thermal stress using weather and physiological variables. Comput Electron Agric 144:37–43. https://doi.org/10.1016/j.compag.2017.11.033
Sun C, Li C, Liu Y, Liu Z, Tan J (2019) Prediction method of concentricity and perpendicularity of aero engine multistage rotors based on PSO-BP neural network. IEEE Access 99:1. https://doi.org/10.1109/access.2019.2941118
Tayarani-Bathaie SS, Khorasani K (2015) Fault detection and isolation of gas turbine engines using a bank of neural networks. J Process Control 36:22–41. https://doi.org/10.1016/j.jprocont.2015.08.007
Wen J, Huang HR, Li HW, Xu GQ, Fu YC (2017) Thermal and hydraulic performance of a compact plate finned tube air-fuel heat exchanger for aero-engine. Appl Therm Eng 2017:126. https://doi.org/10.1016/j.applthermaleng.2017.07.103
Werner Kristjanpoller R, Esteban Hernandez P (2017) Volatility of main metals forecasted by a hybrid ANN-GARCH model with regressors. Expert Syst Appl 84:290–300. https://doi.org/10.1016/j.eswa.2017.05.024
Yameqani AS, Alesheikh AA (2019) Predicting subjective measures of walkability index from objective measures using artificial neural networks. Sustain Cities Soc 48:101560. https://doi.org/10.1016/j.scs.2019.101560
Zheng Q, Fang J, Hu Z, Zhang H (2019) Aero-engine on-board model based on batch normalize deep neural network. IEEE Access 99:1. https://doi.org/10.1109/access.2018.2885199
Zhou HW, Huang JQ, Lu F (2017) Reduced kernel recursive least squares algorithm for aero-engine degradation prediction. Mech Syst Signal Process 95:446–467. https://doi.org/10.1016/j.ymssp.2017.03.046
Acknowledgements
This work was supported by Shaanxi Natural Science Foundation (No. 2017JQ6034).
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Xie, C., Zhang, P. & Yan, Z. Correlation analysis of aeroengine operation monitoring using deep learning. Soft Comput 25, 551–562 (2021). https://doi.org/10.1007/s00500-020-05166-2
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DOI: https://doi.org/10.1007/s00500-020-05166-2