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Data-Driven Intelligent Predictive Maintenance of Industrial Assets

  • Olga FinkEmail author
Chapter
Part of the Women in Engineering and Science book series (WES)

Abstract

Condition monitoring and predictive maintenance enable to focus on the operating and degradation conditions of a specific asset and designing maintenance policies that are optimal for the single asset and not for an entire fleet or a population of similar assets. While this was already possible for critical systems such as power plants for decades, decreased costs of condition monitoring solutions enabled a tight health monitoring also for less critical devices, enabling thereby an improved availability of the assets and decreased life cycle costs. With the increased accessibility to large amounts of condition monitoring data, the challenges of the developed predictive maintenance applications have also increased. The book chapter provides an introduction to predictive maintenance and the current state of knowledge in the field, particularly focusing on the data-driven approaches. It points out challenges and finally presents approaches that overcome some of the existing challenges.

References

  1. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828CrossRefGoogle Scholar
  2. Chen Z, Li W (2017) Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network. IEEE Trans Instrumen Meas 66(7):1693–1702CrossRefGoogle Scholar
  3. Chen Z, Li C, Sanchez R-V (2015) Gearbox fault identification and classification with convolutional neural networks. Shock Vib 2015:1–10Google Scholar
  4. Forman G (2003) An extensive empirical study of feature selection metrics for text classification. J Mach Learn Res 3:1289–1305zbMATHGoogle Scholar
  5. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680Google Scholar
  6. Hu Y, Palmé T, Fink O (2017) Fault detection based on signal reconstruction with auto-associative extreme learning machines. Eng Appl Artif Intell 57:105–117CrossRefGoogle Scholar
  7. Kadry S (2013) Diagnostics and prognostics of engineering systems: methods and techniques. Engineering Science Reference, p 433CrossRefGoogle Scholar
  8. Khan S, Yairi T (2018) A review on the application of deep learning in system health management. Mech Syst Signal Process 107:241–265CrossRefGoogle Scholar
  9. Krummenacher G, Ong CS, Koller S, Kobayashi S, Buhmann JM (2018) Wheel defect detection with machine learning. IEEE Trans Intell Transp Syst 19(4):1176–1187CrossRefGoogle Scholar
  10. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRefGoogle Scholar
  11. Lee J, Wu F, Zhao W, Ghaffari M, Liao L, Siegel D (2014) Prognostics and health management design for rotary machinery systems—reviews. Method Appl Mech Syst Signal Process 42(1):314–334CrossRefGoogle Scholar
  12. Li X, Ding Q, Sun J-Q (2018) Remaining useful life estimation in prognostics using deep convolution neural networks. Reliab Eng Syst Saf 172:1–11CrossRefGoogle Scholar
  13. Meyer T, Sextro W (2014) Closed-loop control system for the reliability of intelligent mechatronic systems. In: European conference of the prognostics and health management society, vol 5Google Scholar
  14. Michau G, Palmé T, Fink O (2017) Deep feature learning network for fault detection and isolation. In: Conference of the PHM societyGoogle Scholar
  15. Michau G, Palmé T, Fink O (2018) Fleet PHM for critical systems: bi-level deep learning approach for fault detection. In: Proceedings of the European conference of the PHM society, vol 4. Utrecht, The NetherlandsGoogle Scholar
  16. Remadna I, Terrissa SL, Zemouri R, Ayad S (2018) An overview on the deep learning based prognostic. In: 2018 International conference on advanced systems and electric technologies (IC_ASET), IEEE, pp 196–200Google Scholar
  17. Sateesh Babu G, Zhao P, Li X-L (2016) Deep convolutional neural network based regression approach for estimation of remaining useful life. Springer, Cham, pp 214–228Google Scholar
  18. Tang J, Deng C, Huang G-B (2016) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst 27(4):809–821MathSciNetCrossRefGoogle Scholar
  19. Thomas É, Levrat É, Iung B, Cocheteux P (2009) Opportune maintenance and predictive maintenance decision support. In: IFAC proceedings volumes, vol 42, no 4, pp 1603–1608CrossRefGoogle Scholar
  20. van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(Nov):2579–2605zbMATHGoogle Scholar
  21. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–3408MathSciNetzbMATHGoogle Scholar
  22. Yan W, Yu L (2015) On accurate and reliable anomaly detection for gas turbine combustors: a deep learning approach. In: Proceedings of the annual conference of the prognostics and health management societyGoogle Scholar
  23. Yam RCM, Tse P, Li L, Tu P (2001) Intelligent predictive decision support system for condition-based maintenance. Int J Adv Manuf Technol 17(5):383–391CrossRefGoogle Scholar
  24. Yoon AS, Lee T, Lim Y, Jung D, Kang P, Kim D, Park K, Choi Y (2017) Semi-supervised learning with deep generative models for asset failure prediction. arXiv: 1709.00845Google Scholar
  25. Zhang C, Lim P, Qin AK, Tan KC (2017) Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE Trans Neural Netw Learn Syst 28(10):2306–2318CrossRefGoogle Scholar
  26. Zhang J, Wang P, Yan R, Gao RX (2018) Deep learning for improved system remaining life prediction. Proc CIRP 72:1033–1038CrossRefGoogle Scholar
  27. Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX (2016) Deep learning and its applications to machine health monitoring: a survey. arXiv: 1612.07640Google Scholar
  28. Zhao R, Yan R, Wang J, Mao K (2017) Learning to monitor machine health with convolutional Bi-directional LSTM networks. Sensors 17(2):273 (Switzerland)CrossRefGoogle Scholar
  29. Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Chair of Intelligent Maintenance SystemsETH ZürichZurichSwitzerland

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