Along with the rapid growth of the wind energy sector, reducing the wind energy costs caused by unplanned downtimes and maintenances has aroused great concern of researchers. Condition monitoring system (CMS) is widely used for detecting anomalies of wind power plants (WPPs) so as to reduce the downtimes and optimize the maintenance plan. However, current solutions to condition monitoring of WPPs focus mostly on detecting a particular anomaly on a single component or a subsystem. Optimizing the maintenance plan of whole wind power plant requires a solution to system level condition monitoring of WPPs.This paper gives a procedure for system level condition monitoring of WPPs using data driven method, that provides an overall picture of the system statuses. Firstly, cluster analysis is utilized to automatically learn the normal behavior model of WPPs from the observations. Two clustering algorithms are explored to choose a suitable one for modeling the WPPs. The presented anomaly detection algorithm uses the learned model as reference to detect the system anomalies. The effectiveness of this approach is evaluated with real world data.

This paper presents an approach for exploiting multicore hardware architectures on coding level for the IEC 61131-3. An interface between the IEC 61131-3 code and software of a different programming language outsources the actual parallel workload. For validation purpose, an embedded multicore hardware is used as a controlling device, which executes software for the use case of model based condition monitoring. The case study results show an explicit benefit of the multicore exploiting software in comparison to its singlecore counterpart, which is reflected with a faster processing of up to a factor of 3. Overall, this approach can be used for developing high performance applications or for accelerating existing applications in industry

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Eine zuverlässige Prozessüberwachung ermöglicht es, Kosten und Risiken zu reduzieren, indem Fehler und Probleme im Prozessablauf frühzeitig erkannt und im besten Fall ein Produktionsstopp der Anlage vermieden wird. Im Beitrag werden stochastische Methoden zur Fehlererkennung behandelt. Die betrachteten Methoden basieren auf der Auswertung der Wahrscheinlichkeit fehlerhafter Messwerte hinsichtlich gegebener stochastischer Modelle. Verschiedene stochastische Modelle werden bezüglich ihrer Vor- und Nachteile untersucht und in experimentellen Untersuchungen evaluiert.

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