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Full-Cycle Failure Analysis Using Conventional Time Series Analysis and Machine Learning Techniques

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Abstract

The paper studies time series of dynamical systems for failures, applying data-driven machine learning techniques, such as clustering and tipping point analysis. Artificial data with known properties and real systems case studies are considered, with diverse patterns of time series. Applicability of various techniques is discussed. The proposed methodology may be useful in industrial and geophysical applications, where sensor records are available for data-driven failure analysis.

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References

  1. P. Lara-Benitez, M. Carranza-Garcia, J. Riquelme, An experimental review on deep learning architectures for time series forecasting. Int. J. Neural Syst. 31(3), 2130001 (2021)

    Article  Google Scholar 

  2. X. Sun, K. Chakrabarty, R. Huang, Y. Chen, B. Zhao, H. Cao, Y. Han, X. Liang, L. Jiang, System-level hardware failure prediction using deep learning. In: Proceedings of the 56th Annual Design Automation Conference 2019, Las Vegas, NV, USA (2019)

  3. C. Su, L. Tsai, S. Huang, Y. Li, Deep learning-based real-time failure detection of storage devices, international conference on applied human factors and ergonomics, advances in artificial intelligence, software and systems. Engineering 965, 160–168 (2019)

    Google Scholar 

  4. T. Lenton et al., Tipping elements in the Earth’s climate system. Proc. Natl. Acad. Sci. USA 105(6), 1786–1793 (2008)

    Article  CAS  Google Scholar 

  5. V. Livina, F. Kwasniok, G. Lohmann, J. Kantelhardt, T. Lenton, Changing climate states and stability: from Pliocene to present. Clim. Dyn. 37(11–12), 2437–2453 (2011)

    Article  Google Scholar 

  6. V. Livina, F. Kwasniok, T. Lenton, Potential analysis reveals changing number of climate states during the last 60 kyr. Clim. Past 6, 77–82 (2010)

    Article  Google Scholar 

  7. V. Livina, T. Lenton, A modified method for detecting incipient bifurcations in a dynamical system. Geophys. Res. Lett. 34, L03712 (2007)

    Article  Google Scholar 

  8. V. Livina, E. Barton, A. Forbes, Tipping point analysis of the NPL footbridge. J. Civ. Struct. Heal. Monit. 4, 91–98 (2014)

    Article  Google Scholar 

  9. M. Perry, V. Livina, P. Niewczas, Tipping point analysis of cracking in reinforced concrete. Smart Mater. Struct. 25(1), 015027 (2016)

    Article  Google Scholar 

  10. V. Livina, A. Lewis, M. Wickham, Tipping point analysis of electrical resistance data with early warning signals of failure for predictive maintenance. J. Electron. Test. 36, 569–576 (2020)

    Article  Google Scholar 

  11. J. Prettyman, T. Kuna, V. Livina, Generalized early warning signals in multivariate and gridded data with an application to tropical cyclones. Chaos 29, 073105 (2019)

    Article  CAS  Google Scholar 

  12. G. Gan, C. Ma, J. Wu, Data Clustering: Theory, Algorithms, and Applications (SIAM, New Delhi, vol, 2007), p. 20

    Book  Google Scholar 

  13. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction Springer Series in Statistics. (Springer, Berlin, 2008)

    Google Scholar 

  14. J. Bruno, I. Weiss, Metric axioms: a structural study. Topol. Proceed. 4 (2015)

  15. Z. Chen, S. Ding, K. Zhang, Z. Li, Z. Hu, Canonical correlation analysis-based fault detection methods with application to alumina evaporation process. Control. Eng. Pract. 46, 51–58 (2016)

    Article  Google Scholar 

  16. Z. Chen, S. Ding, K. Zhang, C. Yang, T. Peng, Generalised CCA with applications for fault detection and estimation, IEEE 7th Data Driven Control and Learning Systems Conference, Enshi (China), May 25–27, pp. 545–550 (2018)

  17. Z. Chen, Canonical correlation analysis-based fault detection and process monitoring algorithm MATLAB Central File Exchange (Accessed on 27 Feb 2022) (2020)

  18. V. Livina, G. Lohmann, M. Mudelsee, T. Lenton, Forecasting the underlying potential governing the time series of a dynamical system. Phys. A 392(18), 3891–3902 (2013)

    Article  Google Scholar 

  19. G.E.P. Box, G. Jenkins, Time Series Analysis: Forecasting and Control (Holden-Day, San Francisco, 1976)

    Google Scholar 

  20. H. Bozdogan, Model selection and Akaike’s information criterion (AIC): the general theory and its analytical extensions. Psychometrika 52(2), 345–370 (1987)

    Article  Google Scholar 

  21. E. Clement, Using normalized Bayesian information criterion (BIC) to improve Box-Jenkins model building. Am. J. Math. Stat. 4(5), 214–221 (2014)

    Google Scholar 

  22. S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  CAS  Google Scholar 

  23. Li. Yi, H. Cao, Prediction for tourism flow based on LSTM neural network. Procedia Comput. Sci. 129, 277–283 (2018)

    Article  Google Scholar 

  24. V. Livina, P. Ditlevsen, T. Lenton, An independent test of methods of detecting system states and bifurcations in time-series data. Phys. A 391(3), 485–496 (2012)

    Article  Google Scholar 

  25. H.A. Makse, S. Havlin, M. Schwartz, E. Stanley, Method for generating long-range correlations for large systems. Phys. Rev. E 53(5), 5445–5449 (1996)

    Article  CAS  Google Scholar 

  26. G. Sonnenfeld , K. Goebel, J. Celaya, An agile accelerated aging, characterization, and scenario simulation system for gate controlled power transistors, IEEE Autotestcon, 208–215 (2008)

  27. NASA Prognostic data repository, Set 8, thermal aging experiment. https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/ (Accessed on 27 Feb 2022)

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Acknowledgments

The paper was developed during the 1-year industrial BSc placement of B.Billuroglu at National Physical Laboratory, funded by Surrey University and the Department for Business, Energy and Industrial Strategy (UK). The idea of studying NASA data was suggested by James Blakesley (NPL). The potential artificial data were simulated by PhD student Joaquin Mesa during his placement at NPL. The authors are grateful to Dr. Greg Sonnenfeld (formerly at NASA) for the useful discussion of the thermal aging experiment, whose public data were used in the paper.

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  1. National Physical Laboratory, Teddington, UK

    B. Billuroglu & V. N. Livina

  2. Surrey University, Guildford, UK

    B. Billuroglu

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  1. B. Billuroglu
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  2. V. N. Livina
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Correspondence to V. N. Livina.

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Billuroglu, B., Livina, V.N. Full-Cycle Failure Analysis Using Conventional Time Series Analysis and Machine Learning Techniques. J Fail. Anal. and Preven. 22, 1121–1134 (2022). https://doi.org/10.1007/s11668-022-01381-1

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  • DOI: https://doi.org/10.1007/s11668-022-01381-1

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