Abstract
In the era of digital transformation of factories, one of the most challenging applications of the Industrial Internet of Things (IIoT) is predictive maintenance. This paper presents a holistic concept for predictive maintenance together with a reference architecture that includes data acquisition on the sensor level, edge computing and digital twin applications. For that purpose, condition-based maintenance, lifecycle monitoring and digital assistance systems are integrated to develop application-specific digital twins based on the proposed architecture, integrating heterogenous data sources in order to enhance the accuracy of the machine learning models. The concept is illustrated through an experimental use case.
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