Gas turbine power plants generate an ever growing amount of high frequency dynamic sensor data. One of the applications of this data is the protection against problems induced by combustion dynamics, as, e.g., with the ArgusOMDS system developed by IfTA. In the light of digitalization, this data has the potential to also be used in other areas and ultimately transform maintenance, repair and overhaul approaches. However, current solutions are not designed to cope with the large time windows needed for a general analysis and this can hinder development of advanced machine analysis algorithms. In this work, we present an end-to-end approach for large scale sensor measurement analysis, employing data mining techniques and enabling machine learning algorithms. Our approach covers the complete data pipeline from sensor measurement acquisition to analysis and visualization. We demonstrate the feasibility of our approach by presenting several case studies that prove the benefits over existing solutions.

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