![]() We collected 10000 multiple day segments of multiple identical and different machines. This seasonality classes can be used for insight into intra and inter group behaviour between machines and add causality to factory wide correlations. ![]() To describe the signal and boost the forecast results we use a clustering method to group each unknown data stream in a seasonality class. Classical approaches, such as ARIMA and Exponential Smoothing can be used for forecasting. In theory every time series, that is currently monitored by for a breach of thresholds, can be extended with a forecast method. This poses an opportunity to apply methods in real time to support the reliability of production machines. This data is getting more accessible by higher network and computing capabilities. ![]() Huge amounts of data are produced inside an industrial production plant every minute. This mapping aids practitioners in understanding how our description framework relates to AAS, potentially aiding in description or implementation activities. Furthermore, we provide a mapping from our description framework to the Asset Administration Shell (AAS) which is an emerging standard for Industry 4.0 system integration. This report provides an extended example of reporting to highlight the utility of this description framework, focusing on the DT of an industrial drilling machine. Our previous work developed a DT description framework with fourteen characteristics as a checklist for experience report authors to better describe the capabilities of their DT projects. A lack of these details could therefore hamper both understanding of these DTs and development of DT tools and techniques. However, these experience reports often leave out essential characteristics of the DT, such as the scope of the system-under-study, the insights and actions enabled, and the time-scale of processing. The pace of reporting on Digital Twin (DT) projects continues to accelerate both in industry and academia.
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