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Durch die zunehmende Digitalisierung von Maschinen werden in der Produktion immer größere Mengen an Daten aufgezeichnet. Um diese Daten sinnvoll nutzen zu können, bieten sich datengetriebene Verfahren an, die aber durch die spezifischen Randbedingungen in der Produktion oft scheitern. Hier liegt die Lösung in derKombination datengetriebener Verfahren mit vorhandenem Expertenwissen. Dieser Artikel beschreibt die Voraussetzungen für eine erfolgreiche Datenanalyse im Produktionsumfeld und einen industriellen Datenanalyseprozess am Beispiel des SIDAP-Projekts.

Liquid leakage from pipelines is a critical issue in large-scale chemical process plants since it can affect the normal operation of the plant and pose unsafe and hazardous situations. Therefore, leakage detection in the early stages can prevent serious damage. Developing a vision-based inspection system by means of IR imaging can be a promising approach for accurate leakage detection. IR cameras can capture the effect of leaking drops if they have higher (or lower) temperature than their surroundings. Since the leaking drops can be observed in an IR video as a repetitive phenomenon with specific patterns, motion pattern detection methods can be utilized for leakage detection. In this paper, an approach based on the Kalman filter is proposed to track the motion of leaking drops and differentiate them from noise. The motion patterns are learned from the training data and applied to the test data to evaluate the accuracy of the method. For this purpose, a laboratory demonstrator plant is assembled to simulate the leakages from pipelines, and to generate training and test videos. The results show that the proposed method can detect the leaking drops by tracking them based on obtained motion patterns. Furthermore, the possibilities and conditions for applying the proposed method in a real industrial chemical plant are discussed at the end.

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