Evaluation of the aeroengine performance reliability based on generative adversarial networks and Weibull distribution

Abstract

Evaluating the reliability of aeroengines with few or zero failures through the use of the Weibull distribution has become a key problem in aviation, as the difficulty of obtaining sufficient sample sizes of monitoring data limits the reliability of evaluations. This paper aims to solve this problem by using generative adversarial networks to generate aeroengine condition monitoring data and increase the volume of usable data. The experimental results show that after a large number of network training epochs, the generated data can reflect the regularity of empirical monitoring data. The relationship between monitoring parameters and Weibull distribution parameters is determined, the generated data are used to estimate Weibull parameters, and the Weibull distribution reliability function for aeroengines is established. The results indicate that the combined use of generative adversarial networks and the Weibull distribution reliability function can indeed solve the problem of limited data volume, and that this method can improve the accuracy of aeroengine reliability assessment. The effectiveness of the proposed method is verified by analyzing monitoring data and time on wing data of 30 aeroengines, calculating engine reliability, and comparing the results with those yielded from the generated data.

Keywords

Aeroengine generative adversarial networks Weibull distribution reliability

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References

Santhosh

Gopika

Ghosh

, et al. An approach for reliability prediction of instrumentation & control cables by artificial neural networks and Weibull theory for probabilistic safety assessment of NPPs. Reliab Eng Syst Safety 2018; 170: 31–44.

Manoj

Faton

. Bayesian estimation for parameters and reliability characteristic of the Weibull Rayleigh distribution. J King Saud Univ Sci 2017.

Awad

. Economic allocation of reliability growth testing using Weibull distributions. Reliab Eng Syst Safety 2016; 152: 273–280.

Jia

Wang

Jiang

, et al. Inference on the reliability of Weibull distribution with multiply Type-I censored data. Reliab Eng Syst Safety 2016; 150: 171–181.

Mozhgan

Abdolreza

Mahdi

, et al. Interaction between Weibull parameters and mechanical strength reliability of industrial-scale water gas shift catalysts. Particuology 2017; 32: 160–166.

Xua

Droguetta

Linsc

, et al. On the q-Weibull distribution for reliability applications: an adaptive hybrid artificial bee colony algorithm for parameter estimation. Reliab Eng Syst Safety 2017; 158: 93–105.

Naresh

Shankar

Velmurugan

. Reliability analysis of tensile strengths using Weibull distribution in glass/epoxy and carbon/epoxy composites. Compos Part B: Eng 2018; 133: 129–144.

Goodfellow I, Pouget-Abadie J and Mirza M. Generative adversarial networks. In: Proceedings of the conference on advances in neural information processing systems, Montreal, Quebec, Canada, December 8-13, 2014, pp.2672–2680.

Radford A, Metz L and Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint: 1511.06434, 2015.

10.

Reinhard

Adhikhmin

Gooch

, et al. Color transfer between images. IEEE Comput Graphics Appl 2001; 21: 34–41.

11.

Larsson G, Maire M and Shakhnarovich G. Learning representations for automatic colorization. In: European conference on computer vision. New York: Springer International Publishing, 2016, pp.577–593.

12.

Ledig C, Theis L, Huszar F, et al. Photo-realistic single image super-resolution using a generative adversarial network. arXiv preprint: 1609.04802, 2017.

13.

Li J, Monroe W, Shi T, et al. Adversarial learning for neural dialogue generation. arXiv preprint: 1701.06547, 2017.

14.

Yu L T, Zhang W N, Wang J, et al. SeqGAN: sequence generative adversarial nets with policy gradient. arXiv preprint: 1609.05473, 2017.

15.

Hu WW and Tan Y. Generating adversarial malware examples for black-box attacks based on GAN. arXiv preprint: 1702.05983, 2017.

16.

Chidambaram M and Qi YJ. Style transfer generative adversarial networks: learning to play chess differently. arXiv preprint: 1702.06762, 2017.

17.

Wang

. Residual useful life prediction for aircraft engine based on information fusion. J Aerosp Power 2012; 27: 2749–2755.

18.

Wang

. An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition. Neurocomputing 2018; 310: 213–222.

19.

Chen

, et al. Generative adversarial networks for parallel transportation systems. IEEE Intell Transport Syst Mag 2018; 10: 4–10.

20.

Ren

Zuo

. Real-time performance reliability prediction for civil aviation aircraft engines based on multiple performance measures. J Aerosp Power 2010; 25: 2811–2815.

21.

Wang

Gao

. Reliability analysis on aero-engine using Bayesian model averaging. J Aerosp Power 2014; 29: 305–313.

22.

Ren SH. Research on methods of performance reliability assessments and life on wing prediction for civil aeroengine. Dissertation, Nanjing University of Aeronautics and Astronautics, China, 2010.

23.

Samli

Yucel

. Global robust stability analysis of uncertain neural networks with time varying delays. Neurocomputing 2015; 167: 371–377.

24.

Fernandez

Caldero

Diez

. Implementation in MATLAB of the adaptive Monte Carlo method for the evaluation of measurement uncertainties. Accred Qual Assur 2009; 14: 95–106.

25.

Dai

Wang

Jiao

. Bayes Monte-Carlo assessment method of protection systems reliability based on small failure sample data. IEEE Trans Power Deliv 2014; 29: 1841–1848.

26.

Nieto

PJG

García-Gonzalo

Lasheras

, et al. Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability. Reliab Eng Syst Safety 2015; 138: 219–231.

27.

Han

Guan

Huang

. Check node reliability-based scheduling for BP decoding of non-binary LDPC codes. IEEE Trans Commun 2013; 61: 877–885.