Machine Learning Method for Spinning Cyber-Physical Production System Subject to Condition Monitoring

Author:

Farooq Basit,Bao Jinsong

Publisher

Springer International Publishing

Reference9 articles.

1. 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

2. Springer Series in Wireless Technology;S Jeschke,2017

3. Yan, R., Gao, R.X., Chen, X.: Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process. 96, 1–15 (2014)

4. 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)

5. 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

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