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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akbar, A., Khan, A., Carrez, F., Moessner, K.: Predictive analytics for complex IoT data streams. IEEE Internet Things J. 4, 1571–1582 (2017)
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)
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)
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)
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)
Chang, C.-C., Lin Libsvm, C.J.: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 20–29 (2013)
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)
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)
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)
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)
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)
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)
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)
Xie, Z., Jin, Y.: An extended reinforcement learning framework to model cognitive development with enactive pattern representation. IEEE Trans. Cogn. Dev. Syst. (2018)
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)
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)
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)
Chen, M., Tian, Y., Fortino, G., Zhang, J., Humar, I.: Cognitive internet of vehicles. Elsevier Comput. Commun. 120, 58–70 (2018)
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)
Suna, R., Zhangb, X.: Top-down versus bottom-up learning in cognitive skill acquisition. Elsevier Cogn. Syst. Res. 5, 63–89 (2004)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Poosapati, V., Katneni, V., Manda, V.K., Ramesh, T.L.V. (2019). Enabling Cognitive Predictive Maintenance Using Machine Learning: Approaches and Design Methodologies. In: Wang, J., Reddy, G., Prasad, V., Reddy, V. (eds) Soft Computing and Signal Processing . Advances in Intelligent Systems and Computing, vol 898. Springer, Singapore. https://doi.org/10.1007/978-981-13-3393-4_5
Download citation
DOI: https://doi.org/10.1007/978-981-13-3393-4_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3392-7
Online ISBN: 978-981-13-3393-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)