Abstract
We present a new framework for cognitive maintenance (CM) based on cyber-physical systems and advanced artificial intelligence techniques. These CM systems integrate intelligent deep learning approaches and intelligent decision-making techniques, which can be used by maintenance professionals who are working with cutting-edge equipment. The systems will provide technical solutions to real-time online maintenance tasks, avoid outages due to equipment failures, and ensure the continuous and healthy operation of equipment and manufacturing assets. The implementation framework of CM consists of four modules, i.e., cyber-physical system, Internet of Things, data mining, and Internet of Services. In the data mining module, fault diagnosis and prediction are realized by deep learning methods. In the case study, the backlash error of cutting-edge machine tools is taken as an example. We use a deep belief network to predict the backlash of the machine tool, so as to predict the possible failure of the machine tool, and realize the strategy of CM. Through the case study, we discuss the significance of implementing CM for cutting-edge equipment, and the framework of CM implementation has been verified. Some CM system applications in manufacturing enterprises are summarized.
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Bao-rui LI, Yi WANG, Guo-hong DAI, and Ke-sheng WANG declare that they have no conflict of interest.
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Li, Br., Wang, Y., Dai, Gh. et al. Framework and case study of cognitive maintenance in Industry 4.0. Front Inform Technol Electron Eng 20, 1493–1504 (2019). https://doi.org/10.1631/FITEE.1900193
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DOI: https://doi.org/10.1631/FITEE.1900193
Key words
- Cognitive maintenance
- Industry 4.0
- Cutting-edge equipment
- Deep learning
- Green monitor
- Smart manufacturing factory