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Enabling Cognitive Predictive Maintenance Using Machine Learning: Approaches and Design Methodologies

  • Vijayaramaraju PoosapatiEmail author
  • Vedavathi Katneni
  • Vijaya Killu Manda
  • T. L. V. Ramesh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 898)

Abstract

Asset reliability and 100% availability of machines are a competitive business advantage in complex industrial environment as they play a vital role in improving productivity. Preventive maintenance models help to identify the performance degradation or failure of machines well ahead to prevent unscheduled breakdown of machines. However, lack of knowledge in identifying the root cause or the lack of knowledge to fix the problem may delay the corrective actions, which in turn impacts the productivity. To overcome this problem, cognitive predictive maintenance model is proposed which helps in classifying and recommending corrective actions along with predicting time to failure of machine. We discussed in detail about building a cognitive system using rule-based bottom-up approaches. We also presented the high-level design of a system to build a software solution using open-source technologies.

Keywords

Industrial Internet of things Machine learning Time to failure Cognitive predictive model 

References

  1. 1.
    Akbar, A., Khan, A., Carrez, F., Moessner, K.: Predictive analytics for complex IoT data streams. IEEE Internet Things J. 4, 1571–1582 (2017)CrossRefGoogle Scholar
  2. 2.
    Ayoubi, S., Limam, N., Salahuddin, M.A., Shahriar, N., Boutaba, R., Estrada-Solano, F., Caicedo, O.M.: Machine Learning for Cognitive Network Management. IEEE Commun. Mag. 56(1), 158–165 (2018)CrossRefGoogle Scholar
  3. 3.
    Wang, J., Li, C., Han, S., Sarkar, S., Zhou, X.: Predictive maintenance based on event-log analysis: a case study. IBM J. Res. Dev. 61, 11:121–11:132 (2017)CrossRefGoogle Scholar
  4. 4.
    Goyal, A., Aprilia, E., Janssen, G., Kim, Y., Kumar, T., Mueller, R., Phan, D., Raman, A., Schuddebeurs, J., Xiong, J., Zhang, R.: Asset health management using predictive and prescriptive analytics for the electric power grid. IBM J. Res. Dev. 60, 4.1–4.14 (2016)CrossRefGoogle Scholar
  5. 5.
    Susto, G.A., Schirru, A., Pampuri, S., McLoone, S., Beghi, A.: Machine learning for predictive maintenance, a multiple classifier approach. IEEE Trans. Ind. Inform. 11(3), 812–820 (2015)CrossRefGoogle Scholar
  6. 6.
    Chang, C.-C., Lin Libsvm, C.J.: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 20–29 (2013)Google Scholar
  7. 7.
    Ren, L., Sun, Y., Wang, H., Zhang, L.: Prediction of bearing remaining useful life with deep convolution neural network. IEEE Access 6, 13041–13049 (2018)CrossRefGoogle Scholar
  8. 8.
    Deutsch, J., He, D.: Using deep learning-based approach to predict remaining useful life of rotating components. IEEE Trans. Syst. Man Cybern. Syst. 48, 11–20 (2017)CrossRefGoogle Scholar
  9. 9.
    Dhoolia, P., Chugh, P., Costa, P., Gantayat, N., Gupta, M., Kambhatla, N., Kumar, R., Mani, S., Mitra, P., Rogerson, C., Saxena, M.: A cognitive system for business and technical support: a case study. IBM J. Res. Dev. 61, 7:74–7:85 (2017)CrossRefGoogle Scholar
  10. 10.
    Damerow, F., Knoblauch, A., Korner, U., Eggert, J.: Towards self-referential autonomous learning of object and situation models. Springer Cognit. Comput. 8, 703–719 (2016)CrossRefGoogle Scholar
  11. 11.
    Taher, A.: Rule mining and prediction using the Flek machine—a new machine learning engine. In: Bagheri, E., Cheung, J. (eds.) Advances in Artificial Intelligence. Canadian AI Lecture Notes in Computer Science, vol. 10832. Springer, Cham (2018)CrossRefGoogle Scholar
  12. 12.
    Ahmad, I., Basheri, M., Iqbal, M.J., Rahim, A.: Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection. IEEE Access 6, 33789–33795 (2018)CrossRefGoogle Scholar
  13. 13.
    Zhu, J., Song, Y., Jiang, D., Song, H.: A new deep-Q-Learning-based transmission scheduling mechanism for the cognitive internet of things. IEEE Internet Things J. (2017)Google Scholar
  14. 14.
    Xie, Z., Jin, Y.: An extended reinforcement learning framework to model cognitive development with enactive pattern representation. IEEE Trans. Cogn. Dev. Syst. (2018)Google Scholar
  15. 15.
    Vernon D., von Hofsten C., Fadiga L: The iCub cognitive architecture, a roadmap for cognitive development in humanoid robots. In: Cognitive Systems Monographs, vol. 11. Springer, Berlin, Heidelberg (2017)Google Scholar
  16. 16.
    Oltramari, A., Lebiere, C.: Pursuing artificial general intelligence by leveraging the knowledge capabilities of act-r. In: Artificial General Intelligence, vol. 7716, pp. 199–208. Springer (2012)Google Scholar
  17. 17.
    Ge, Z., Song, Z., Ding, S.X., Huang, B.: Data mining and analytics in the process industry—the role of machine learning. IEEE Access 5, 20590–20616 (2017)CrossRefGoogle Scholar
  18. 18.
    Chen, M., Tian, Y., Fortino, G., Zhang, J., Humar, I.: Cognitive internet of vehicles. Elsevier Comput. Commun. 120, 58–70 (2018)CrossRefGoogle Scholar
  19. 19.
    Poosapati, V., Katneni, V., Manda, V.K.: Super SCADA systems: a prototype for next gen SCADA system. IAETSD J. Adv. Res. Appl. Sci. 3, 107–115 (2018)Google Scholar
  20. 20.
    Suna, R., Zhangb, X.: Top-down versus bottom-up learning in cognitive skill acquisition. Elsevier Cogn. Syst. Res. 5, 63–89 (2004)CrossRefGoogle Scholar
  21. 21.
    Antonio, L., Lebiere, A., Oltramarid, A.: The knowledge level in cognitive architectures, current limitations and possible developments. Elsevier Cogn. Syst. Res. 48, 39–55 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Vijayaramaraju Poosapati
    • 1
    Email author
  • Vedavathi Katneni
    • 1
  • Vijaya Killu Manda
    • 1
  • T. L. V. Ramesh
    • 1
  1. 1.GITAM (Deemed to be University)VisakhapatnamIndia

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