Skip to main content
Springer Nature Link
Log in

Remaining useful life prediction of integrated modular avionics using ensemble enhanced online sequential parallel extreme learning machine

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Integrated modular avionics is the core system of modern aircraft, which hosts almost all kinds of electrical functions. The performance of integrated modular avionics has an immediate influence on flight mission. Remaining useful life prediction is an effective manner to guarantee the safety and reliability of airplane. To satisfy the real-time requirement of integrated modular avionics, the prediction algorithm should have fast learning speed. This paper proposes an ensemble enhanced online sequential parallel extreme learning machine to predict the remaining useful life of integrated modular avionics. Firstly, a network with parallel hidden layers is designed to improve feature extraction. Secondly, to enhance the learning stability, the input weights of the network are determined by using extreme learning machine autoencoder. Thirdly, an updating method is developed for online prediction and an adaptive weight is designed to construct the ensemble online sequential prediction method. The effectiveness and superiority of the proposed method are verified through the standard datasets. Finally, this paper regards intermittent faults as the feature of integrated modular avionics and builds a degradation model by using Lévy Process. The proposed method is applied to remaining useful life prediction of integrated modular avionics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Abbreviations

IMA:

Integrated modular avionics

PHM:

Prognostics and health management

RUL:

Remaining useful life

LSTM:

Long short-term memory

ELM:

Extreme learning machine

OS-ELM:

Online sequential-extreme learning machine

EEOS-PELM:

Ensemble enhanced online sequential-parallel extreme learning machine

ELM-AE:

Extreme learning machine autoencoder

RMSE:

Root mean square error

ReLU:

Rectified linear unit

OFLN:

Online fast learning network

POS-ELM:

Parallel online sequential extreme learning machine

Std:

Standard deviation

MAE:

Mean absolute error

EM:

Electromigration

HCI:

Hot carrier injection

References

  1. Zhou T, Xiong H (2012) Design of energy-efficient hierarchical scheduling for integrated modular avionics systems. Chin J Aeronaut 25:109–114

    Article  Google Scholar 

  2. Gao ZH, Ma CB, She ZY, Dong X (2018) An enhanced deep extreme learning machine for integrated modular avionics health state estimation. IEEE Access 6:65813–65823

    Article  Google Scholar 

  3. Matos HLV (2018) Model-based specification of integrated modular avionics systems using object-process methodology. In: 2018 IEEE/AIAA 37th digital avionics systems conference, pp 1–8

  4. Wang Y, Lei H, Hackett R, Beeby M (2019) Safety assessment process optimization for integrated modular avionics. IEEE Aerosp Electron Syst Mag 34:58–67

    Article  Google Scholar 

  5. Zhou Q, Wang J et al (2020) A two-phase multiobjective local search for the device allocation in the distributed integrated modular avionics. IEEE Access 8:1–10

    Article  Google Scholar 

  6. Mathias B, Emil K, Tomas L et al (2018) An optimization approach for pre-runtime scheduling of tasks and communication in an integrated modular avionic system. Optim Eng 19:977–1004

    Article  MathSciNet  Google Scholar 

  7. Degtyarev AR, Kiselev SK (2017) Hardware reconfiguration algorithm in multiprocessor systems of integrated modular avionics. Russ Aeronaut 60:116–121

    Article  Google Scholar 

  8. Wan JX, Xiang X, Bai XY et al (2013) Performability analysis of avionics system with multilayer HM/FM using stochastic Petri nets. Chin J Aeronaut 26:363–377

    Article  Google Scholar 

  9. Lu YF, Li Q, Pan ZP, Liang SY (2018) Prognosis of bearing degradation using gradient variable forgetting factor RLS combined with time series model. IEEE Access 6:10986–10995

    Article  Google Scholar 

  10. Wang F, Mamo T (2018) A hybrid model based on support vector regression and differential evolution for remaining useful lifetime prediction of lithium-ion batteries. J Power Sources 15:49–55

    Google Scholar 

  11. Li X, Ding Q, Sun JQ (2018) Remaining useful life estimation in prognostics using deep convolution neural networks. Reliab Eng Syst Saf 172:1–11

    Article  Google Scholar 

  12. Huang Y, Tang YF, VanZwieten JX, Xiao XC (2019) An adversarial learning approach for machine prognostic health management. In: 2019 international conference on high performance big data and intelligent systems, pp 163–168

  13. Liu JY, Tian Y, Zhang R, Sun YQ, Wang C (2020) A two-stage generative adversarial networks with semantic content constraints for adversarial example generation. IEEE Access 8:205766–205777

  14. Fedosov E, Koverninsky I et al (2017) Use of real-time operating systems in the integrated modular avionics. Procedia Comput Sci 103:384–387

    Article  Google Scholar 

  15. Huang GB, Zhu QY, Siew C (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

    Article  Google Scholar 

  16. Cheng Y, Zhao D, Wang Y, Pei G (2019) Multi-label learning with kernel extreme learning machine autoencoder. Knowl Based Syst 178:1–10

    Article  Google Scholar 

  17. Peng Y, Kong WZ, Yang B (2017) Orthogonal extreme learning machine for image classification. Neurocomputing 266:458–464

    Article  Google Scholar 

  18. Vanli ND, Sayin MO, Delibalta I, Kozat SS (2017) Sequential nonlinear learning for distributed multiagent systems via extreme learning machines. IEEE Trans Neural Netw Learn 28:546–558

    Article  MathSciNet  Google Scholar 

  19. Lv F, Han M (2019) Hyperspectral image classification based on multiple reduced kernel extreme learning machine. Int J Mach Learn Cybern 10:3397–3405

    Article  Google Scholar 

  20. Wang H, Liu X, Song P, Tu X (2019) Sensitive time series prediction using extreme learning machine. Int J Mach Learn Cybern 10:3371–3386

    Article  Google Scholar 

  21. Kamran J, Rafael G, Noureddine Z (2015) A new multivariate approach for prognostics based on extreme learning machine and fuzzy clustering. IEEE Trans Cybern 12:2626–2639

    Google Scholar 

  22. Liu Z, Cheng Y et al (2018) A method for remaining useful life prediction of crystal oscillators using the Bayesian approach and extreme learning machine under uncertainty. Neurocomputing 305:27–38

    Article  Google Scholar 

  23. Roozbeh R, Shiladitya C, Mehrdad S (2017) Multi-step parallel-strategy for estimating the remaining useful life of batteries. In: 2017 IEEE 30th canadian conference on electrical and computer engineering, pp 1–4

  24. Lu F, Wu J, Huang J, Qiu X (2019) Aircraft engine degradation prognostics based on logistic regression and novel OS-ELM algorithm. Aerosp Sci Technol 84:661–671

    Article  Google Scholar 

  25. Gao ZH, Ma CB, Song D, Liu Y (2019) Deep quantum inspired neural network with application to aircraft fuel system fault diagnosis. Neurocomputing 238:13–23

    Article  Google Scholar 

  26. Zhao L, Zhu J (2019) Learning from correlation with extreme learning machine. Int J Mach Learn Cybern 10:3635–3645

    Article  Google Scholar 

  27. Nguyen TV, Mirza B (2018) Dual-layer kernel extreme learning machine for action recognition. Neurocomputing 260:123–130

    Article  Google Scholar 

  28. Kasun LLC, Zhou H, Huang GB, Chi MV (2013) Representational learning with ELMs for big data. IEEE Intell Syst 28:31–34

    Article  Google Scholar 

  29. Li S, Jiang H, Bai J, Liu Y, Yao Y (2019) Stacked sparse autoencoder and case-based postprocessing method for nucleus detection. Neurocomputing 351:167–179

    Article  Google Scholar 

  30. Tang JX, Deng CW, Huang GB (2016) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst 27:809–821

    Article  MathSciNet  Google Scholar 

  31. Khatab ZE, Hajihoseini A, Ghorashi SA (2018) A fingerprint method for indoor localization using autoencoder based deep extreme learning machine. IEEE Sens Lett 65:1–4

    Article  Google Scholar 

  32. Zeng NY, Zhang H (2017) A switching delayed PSO optimized extreme learning machine for short-term load forecasting. Neurocomputing 240:175–182

    Article  Google Scholar 

  33. Gao ZH, Ma CB, Zhang JF, Xu WJ (2019) Enhanced online sequential parallel extreme learning machine and its application in remaining useful life prediction of integrated modular avionics. IEEE Access 7:183479–183488

  34. Corchs S, Fersini E, Gasparini F (2019) Ensemble learning on visual and textual data for social image emotion classification. Int J Mach Learn Cybern 10:2057–2070

    Article  Google Scholar 

  35. Shao HD, Jiang HK, Lin Y, Li XQ (2018) A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders. Int J Mach Learn Cybern 102:278–297

    Google Scholar 

  36. Modi S, Lin Y, Cheng L, Yang G, Liu L, Zhang WJ (2011) A socially inspired framework for human state inference using expert opinion integration. IEEE-ASME Trans Mechatron 16:874–878

    Article  Google Scholar 

  37. Cai M, Yu XJ, Han B (2015) Adaptive natural gradient learning algorithms for Mackey-Glass chaotic time prediction. Neurocomputing 175:41–45

    Google Scholar 

  38. Zhao JS, Yu XJ (2015) Adaptive natural gradient learning algorithms for Mackey-Glass chaotic time prediction. Neurocomputing 175:41–45

    Article  Google Scholar 

  39. Niu PF, Chen K, Ma YP (2017) Model turbine heat rate by fast learning network with tuning based on ameliorated krill herd algorithm. Knowl Based Syst 118:80–92

    Article  Google Scholar 

  40. Tavares LD, Saldanha RR, Vieira DAG (2015) Extreme learning machine with parallel layer perceptrons. Neurocomputing 166:164–171

    Article  Google Scholar 

  41. Deng GQ, Qiu J, Liu GJ (2014) A stochastic automaton approach to discriminate intermittent from permanent faults. Proc Inst Mech Eng G J Aerosp Eng 228(6):880–888

    Article  Google Scholar 

  42. Gao ZH, Ma CB (2017) An IMA degradation model with intermittent faults for RUL prediction. In: 2017 Prognostics and system health management conference, pp 1–6

  43. Niemiro W (2019) Fixed relative precision estimators of growth rate for compound Poisson and Lévy processes. Stat Probab Lett 153:151–156

    Article  MathSciNet  Google Scholar 

  44. Vladimir U, Irina T (2017) Availability assessment of a telecommunications system with permanent and intermittent faults. In: 2017 IEEE first Ukraine conference on electrical and computer engineering, pp 908–911

  45. Hainaut D (2010) Mutual information for stochastic signals and Lévy processes. IEEE Trans Inf Theory 56:18–24

    Article  Google Scholar 

  46. Liao PJ, Chen CL (2008) A new on-state drain-bias TDDB lifetime model and HCI effect on drain-bias TDDB of ultra thin oxide. In: 2008 IEEE international reliability physics symposium, pp 210–214

  47. Adarsh B, Jennifer MP (2017) Electromigration: lognormal versus Weibull distribution. In: 2017 IEEE international integrated reliability workshop, pp 1–4

  48. Kim NH, Dawn A, Choi JH (2016) Prognostics and health management of engineering systems: an introduction. Springer, Berlin

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi’an, Shaanxi, China

    Gao Zehai & Zhang Jianfeng

  2. School of Aeronautics, Northwestern Polytechnical University, Xi’an, Shaanxi, China

    Ma Cunbao

  3. Development Dept of Device OS, Huawei Consumer Business Group, Xi’an, Shaanxi, China

    Xu Weijun

Authors
  1. Gao Zehai
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ma Cunbao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Zhang Jianfeng
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Xu Weijun
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Gao Zehai.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zehai, G., Cunbao, M., Jianfeng, Z. et al. Remaining useful life prediction of integrated modular avionics using ensemble enhanced online sequential parallel extreme learning machine. Int. J. Mach. Learn. & Cyber. 12, 1893–1911 (2021). https://doi.org/10.1007/s13042-021-01283-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-021-01283-y

Keywords