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Estimate remaining useful life for predictive railways maintenance based on LSTM autoencoder

  • Special issue on Advances of Neural Computing phasing challenges in the era of 4th industrial revolution
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Abstract

Nowadays, frequent maintenance and repair of mechanical equipment whose goal is to deter the suspension time of railway infrastructures are proven to be ineffectual. It also results in loss of reliability as well as consuming unnecessary means and costs, since at least half of the precautionary maintenance activities are considered redundant. Despite this spending, operators are struggling to adequately maintain their assets—resulting in unacceptably frequent delays and cancellations and low levels of satisfaction among rail users. Thanks to the increasing availability and sophistication of advanced analytics, operators have a significant opportunity to create solutions to long-standing maintenance challenges. The role of predictive maintenance and especially that of Design-Out Maintenance constitute the necessary procedure that can predict in time any hardware failures, while reducing the damage or wearing down of the overall operational equipment. This can increase the effectiveness of the railways, while significantly reducing the overall expenditure needed for the repair and maintenance of the industry’s infrastructure. This paper proposes a predictive railways maintenance strategy based on deep learning techniques. Specifically, and in order to achieve the exact remaining useful life of the railway equipment, a hybrid neural architecture of long short-term memory autoencoder network is used. The purpose of this suggested architecture is the automatic feature extraction of dynamic time series and their utilization on a prediction model, which can predict with high accuracy the remaining useful life of the railway’s mechanical equipment.

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References

  1. Cachada A et al. Maintenance 4.0: Intelligent and Predictive Maintenance System Architecture. In: 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), Turin, pp 139–146

  2. Poór P, Basl J, Zenisek D (2019) Predictive Maintenance 4.0 as next evolution step in industrial maintenance development. In: 2019 International Research Conference on Smart Computing and Systems Engineering (SCSE), Colombo, Sri Lanka, pp 245–253.

  3. Zahrah SF, Yusof YA, Kumar K, Sorooshian S (2014) Maintenance in the Era of Industry 4.0, Journal of Management and Science, 4(3), 2014

  4. Li CH, Lau HK (2017) A critical review of product safety in industry 4.0 applications. In: 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, pp 1661–1665

  5. Baqqal Y, El hammoumi M (2018) State of the art in maintenance modelling and simulation approaches for maintenance systems. In: 2018 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD), Marrakech, Morocco, pp 214–218

  6. Duyar A, Merrill W (1992) Fault diagnosis for the Space Shuttle main engine. J Guid Control Dyn 15(2):384–389. https://doi.org/10.2514/3.20847

    Article  Google Scholar 

  7. Shimada J, Sakajo S, A statistical approach to reduce failure facilities based on predictive maintenance. In: 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, pp 5156–5160. https://doi.org/10.1109/IJCNN.2016.7727880

  8. Bousdekis A, Apostolou D and Mentzas G (2019) Predictive Maintenance in the 4th Industrial Revolution: Benefits, Business Opportunities and Managerial Implications. In: IEEE Engineering Management Review. https://doi.org/10.1109/EMR.2019.2958037

  9. Cachada A et al. (2018) Maintenance 4.0: Intelligent and predictive maintenance system architecture. In: 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), Turin, pp 139–146. https://doi.org/10.1109/ETFA.2018.8502489

  10. Abbasi T, Lim KH, Rosli NS, Ismail I, Ibrahim R (2018) Development of predictive maintenance interface using multiple linear regression. In: 2018 International Conference on Intelligent and Advanced System (ICIAS), Kuala Lumpur, 2018, pp 1–5. https://doi.org/10.1109/ICIAS.2018.8540602

  11. Yan W, Zhou J (2017) Predictive modeling of aircraft systems failure using term frequency-inverse document frequency and random forest. In: 2017 IEEE international conference on industrial engineering and engineering management (IEEM), Singapore, 2017, pp 828–831. https://doi.org/10.1109/IEEM.2017.8290007

  12. Xie G et al. (2017) Data-driven approach for the prediction of remaining useful life. In: 2017 7th IEEE International Symposium on Microwave, Antenna, Propagation, and EMC Technologies (MAPE), Xi'an, pp 150–155

  13. Ghimire S, Ghimire S, Subedi S (2019) A study on deep learning architecture and their applications. In: 2019 International Conference on Power Electronics, Control and Automation (ICPECA), New Delhi, India, pp 1–6

  14. Hajiaghayi M, Vahedi E (2019) Code failure prediction and pattern extraction using LSTM networks. In: 2019 IEEE fifth international conference on big data computing service and applications (BigDataService), Newark, CA, USA, pp 55–62

  15. Singh SP, Kumar A, Darbari H, Singh L, Rastogi A, Jain S (2017) Machine translation using deep learning: an overview. In: 2017 International Conference on Computer, Communications and Electronics (Comptelix), Jaipur, pp 162–167

  16. Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast. 22 (4): 679–688. CiteSeerX 10.1.1.154.9771

  17. Saxena A, Goebel K (2008) Turbofan engine degradation simulation data set. NASA Ames Prognostics Data Repository (http://ti.arc.nasa.gov/project/prognostic-data-repository), NASA Ames Research Center, Moffett Field, CA

  18. Wenzhu S, Wenting H, Zufeng X, Jianping C (2019) Overview of one-Class classification. In: 2019 IEEE 4th international conference on signal and image processing (ICSIP), Wuxi, China, pp 6–10

  19. Xing L, Demertzis K, Yang J (2020) Identifying data streams anomalies by evolving spiking restricted Boltzmann machines. Neural Comput Appl 32:6699–6713. https://doi.org/10.1007/s00521-019-04288-5

    Article  Google Scholar 

  20. Demertzis K, Iliadis L, Bougoudis I (2020) Gryphon: a semi-supervised anomaly detection system based on one-class evolving spiking neural network. Neural Comput Appl 32:4303–4314. https://doi.org/10.1007/s00521-019-04363-x

    Article  Google Scholar 

  21. Georgakopoulos SV, Tasoulis SK, Mallis GI et al (2020) Change detection and convolution neural networks for fall recognition. Neural Comput Appl 32:17245–17258. https://doi.org/10.1007/s00521-020-05208-8

    Article  Google Scholar 

  22. Sagheer A, Kotb M (2019) Unsupervised pre-training of a deep LSTM-based stacked autoencoder for multivariate time series forecasting problems. Sci Rep 9:19038. https://doi.org/10.1038/s41598-019-55320-6

    Article  Google Scholar 

  23. Alahakoon D, Nawaratne R, Xu Y et al (2020) Self-building Artificial Intelligence and Machine learning to empower big data analytics in smart cities. Inf Syst Front. https://doi.org/10.1007/s10796-020-10056-x

    Article  Google Scholar 

  24. Demertzis K, Iliadis L (2016) Bio-inspired hybrid intelligent method for detecting android malware. In: Kunifuji S Papadopoulos G, Skulimowski A, Kacprzyk J (eds) Knowledge, information and creativity support systems. Advances in intelligent systems and computing, vol 416. Springer, Cham. https://doi.org/10.1007/978-3-319-27478-2_20

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Liqiang Hu

  2. School of Economics and Management, Lanzhou Jiaotong University, Lanzhou, 730070, China

    Guoyong Dai

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  1. Liqiang Hu
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  2. Guoyong Dai
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Correspondence to Liqiang Hu.

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Hu, L., Dai, G. Estimate remaining useful life for predictive railways maintenance based on LSTM autoencoder. Neural Comput & Applic (2022). https://doi.org/10.1007/s00521-021-06051-1

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