Abstract
This paper shows how automation components can be enhanced with self-monitoring capabilities, which are more effective than traditional rule-based methods, by using Industrial Analytics approaches. Two application examples are presented to show how this approach allows the realization of a predictive maintenance strategy, while drastically reducing the realization effort. Furthermore, the benefits of a flexible architecture combining edge- and cloud-computing for the realization of such monitoring system are discussed.
Keywords
- Industrial Analytics
- Predictive Maintenance
- Machine Learning
- Edge Computing
- Feature Engineering
- Self-Monitoring
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Gatica, C.P., Boschmann, A. (2019). Enabling Self-Diagnosis of Automation Devices through Industrial Analytics. In: Beyerer, J., Kühnert, C., Niggemann, O. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation, vol 9. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58485-9_12
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DOI: https://doi.org/10.1007/978-3-662-58485-9_12
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Publisher Name: Springer Vieweg, Berlin, Heidelberg
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Online ISBN: 978-3-662-58485-9
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