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A novel soft computing method for engine RUL prediction

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Abstract

Prognostics is an engineering discipline focused on predicting the Remaining Useful Life (RUL) of a system or a component using raw multimedia (sensor) data. This paper presents a novel machine learning model for this task, which includes a smart ensemble of gradient boosted trees (GBT) and feed-forward neural networks. It incorporates discussions on the poor performance of MLPs and the need of ensemble models. Initial stages of data exploration and pre-processing are also comprehensively documented. Experiments are performed on the four run-to-failure C-MAPSS datasets defined by the 2008 PHM Data Challenge Competition. It concludes by presenting evaluations of multiple prediction models like MLP, SVR, CNN & gradient boosted trees (GBT). Gradient Boosted Trees are efficient in the sense that they produce an encouraging scoring model with minimum effort and also return feature importance information. The proposed method uses stacking ensemble of feed-forward neural networks and gradient boosted trees, as first level learner, and, a single-hidden layer- fully-connected neural network as the meta learner. This ensemble provides better results than any of the models alone or weighted average of their predictions. The proposed method outperforms MLP, SVR, CNN and GBT.

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References

  1. Babu GS, Zhao P, Li X-L (2016) Deep convolutional neural network based regression approach for estimation of remaining useful life. International conference on database systems for advanced applications. Springer, Cham

  2. Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–127

    Article  MathSciNet  Google Scholar 

  3. Coble J et al (2015) A review of prognostics and health management applications in nuclear power plants. Int J Prog Health Manag 6:016 International Journal of prognostics and health management, submitted

    Google Scholar 

  4. Deutsch J, He D (2016) Using deep learning based approaches for bearing remaining useful life prediction. http://phmsociety.org

  5. Friedman, J (1999) Greedy function approximation: a gradient boosting machine. http://www.salford-systems.com/doc. GreedyFuncApproxSS. pdf

  6. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics

  7. Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics

  8. Guo L et al (2017) A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing 240:98–109

    Article  Google Scholar 

  9. Heimes FO (2008) Recurrent neural networks for remaining useful life estimation. Prognostics and Health Management, 2008. PHM 2008. International Conference on. IEEE, 2008

  10. Hochreiter S, Bengio Y, Frasconi P, & Schmidhuber J (2001) Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. http://jku.at

  11. Khelif R et al (2017) Direct remaining useful life estimation based on support vector regression. IEEE Trans Ind Electron 64(3):2276–2285

    Article  Google Scholar 

  12. der Laan V, Mark J, Polley EC, Hubbard AE (2007) Super learner. Stat Appl Genet Mol Biol 6:1

    MathSciNet  MATH  Google Scholar 

  13. Liao L (2014) Discovering prognostic features using genetic programming in remaining useful life prediction. IEEE Trans Ind Electron 61(5):2464–2472

    Article  Google Scholar 

  14. Lim P, Goh CK, Tan KC (2016) A time window neural network based framework for remaining useful life estimation. Neural Networks (IJCNN), 2016 International Joint Conference on. IEEE

  15. Medjaher K, Zerhouni N, Baklouti J (2013) Data-driven prognostics based on health indicator construction: Application to PRONOSTIA's data. Control Conference (ECC), 2013 European. IEEE

  16. Miao Q et al (2013) Remaining useful life prediction of lithium-ion battery with unscented particle filter technique. Microelectron Reliab 53(6):805–810

    Article  Google Scholar 

  17. Mosallam A, Medjaher K, Zerhouni N (2016) Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction. J Intell Manuf 27(5):1037–1048

    Article  Google Scholar 

  18. Peel L (2008) Data driven prognostics using a Kalman filter ensemble of neural network models. Prognostics and Health Management PHM 2008. International Conference on. IEEE

  19. Ramasso E, Saxena A (2014) Performance benchmarking and analysis of prognostic methods for CMAPSS datasets. Int J Prog Health Manag 5(2):1–15

    Google Scholar 

  20. Rumelhart DE, Hinton GE, Williams RJ (1985) Learning internal representations by error propagation. No. ICS-8506. California Univ San Diego La Jolla Inst for Cognitive Science

  21. Saxena A, Goebel K (2008) Phm08 challenge data set. NASA Ames Prognostics Data Repository http://ti.arc.nasa.gov/project/prognostic-data-repository, NASA Ames Research Center, Moffett Field, (consulted 2014–02-15)

  22. Wang T et al (2008) A similarity-based prognostics approach for remaining useful life estimation of engineered systems. Prognostics and Health Management, 2008. PHM 2008. International Conference on. IEEE

  23. Xinxin X, Qing L, Nong C (2016) Remaining useful life prognostics of aircraft engine based on fusion algorithm. Guidance, Navigation and Control Conference (CGNCC), 2016 I.E. Chinese. IEEE

  24. Yang Z, Baraldi P, Zio E (2016) A comparison between extreme learning machine and artificial neural network for remaining useful life prediction. Prognostics and System Health Management Conference (PHM-Chengdu), IEEE

  25. Zhang C et al (2017) Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE Trans Neural Netw Learn Syst 99:1–13

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology (BHU), Varanasi, UP, India

    Sandip Kumar Singh, Sandeep Kumar & J. P. Dwivedi

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  1. Sandip Kumar Singh
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  2. Sandeep Kumar
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  3. J. P. Dwivedi
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Correspondence to Sandip Kumar Singh.

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Singh, S.K., Kumar, S. & Dwivedi, J.P. A novel soft computing method for engine RUL prediction. Multimed Tools Appl 78, 4065–4087 (2019). https://doi.org/10.1007/s11042-017-5204-x

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  • DOI: https://doi.org/10.1007/s11042-017-5204-x

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