Abstract
Digitalization encapsulates the importance of machine condition monitoring which is subjected to predictive analytics for realizing significant improvements in the performance and reliability of rotating equipment i.e., spinning. This paper presents a machine learning approach for condition monitoring, based on a regularized deep neural network using automated diagnostics for spinning manufacturing. This article contributes a solution to find disturbances in a running system through real-time data sensing and signal to process via industrial internet of things. Because this controlled sensor network may comprise on different critical components of the same type of machines, therefore back propagation neural network based multi-sensor performance assessment and prediction strategy were developed for our system which worked as intelligent maintenance and diagnostic system. It is completely automatic requiring no manual extraction of handcrafted features.
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References
Nikolic, B., Ignjatic, J., Suzic, N., Stevanov, B., Rikalovic, A.: Predictive manufacturing systems in industry 4.0. Trends, benefits and challenges. In: Annals of DAAAM and Proceedings, vol. 28, 1 January 2017
Jeschke, S., Brecher, C., Meisen, T., Özdemir, D., Eschert, T.: Industrial internet of things and cyber manufacturing systems. In: Jeschke, S., Brecher, C., Song, H., Rawat, D.B. (eds.) Industrial Internet of Things. SSWT, pp. 3–19. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-42559-7_1
Yan, R., Gao, R.X., Chen, X.: Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process. 96, 1–15 (2014)
Uhlemann, T.H-J., Lehmann, C., Steinhilper, R.: The digital twin: realizing the cyber-physical production system for industry 4.0. In: Procedia CIRP, vol. 61, pp. 335–340 (2017)
Picard, A., Anderl, R.: The integrated component data model for smart production planning. In: Proceedings of the 19th International Seminar on High Technology, pp. 1–6, October 2014
Sun, C., Ma, J., Yao, Q.: On the architecture and development life cycle of secure cyber-physical systems. J. Commun. Inf. Netw. 1(4), 1–21 (2016)
Saha, S., et al.: Distributed prognostic health management with gaussian process regression. In: 2010 IEEE Aerospace Conference. IEEE (2010)
Karaboga, D., Akay, B., Ozturk, C.: Artificial Bee Colony (ABC) Optimization algorithm for training feed-forward neural networks. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds.) MDAI 2007. LNCS (LNAI), vol. 4617, pp. 318–329. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73729-2_30
Zhang, L., et al.: Automated quality assessment of cardiac MR images using convolutional neural networks. In: Tsaftaris, S., Gooya, A., Frangi, A., Prince, J. (eds.) SASHIMI 2016. LNCS, pp. 138–145. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46630-9_14
Acknowledgement
This research was financially supported by the Fundamental Research Funds for the Central Universities (Grant No. 2232017A-03).
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Farooq, B., Bao, J. (2019). Machine Learning Method for Spinning Cyber-Physical Production System Subject to Condition Monitoring. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2019. Lecture Notes in Computer Science(), vol 11792. Springer, Cham. https://doi.org/10.1007/978-3-030-30949-7_28
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DOI: https://doi.org/10.1007/978-3-030-30949-7_28
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