Turning dynamic sensor measurements from gas turbines into insights: A big data approach

Roman Karlstetter, Robert Widhopf-Fenk, Jakob Hermann, Driek Rouwenhorst, Amir Raoofy, Carsten Trinitis, Martin Schulz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationCeramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791858677
DOIs
StatePublished - 2019
EventASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition, GT 2019 - Phoenix, United States
Duration: 17 Jun 201921 Jun 2019

Publication series

NameProceedings of the ASME Turbo Expo
Volume6

Conference

ConferenceASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition, GT 2019
Country/TerritoryUnited States
CityPhoenix
Period17/06/1921/06/19

Fingerprint

Dive into the research topics of 'Turning dynamic sensor measurements from gas turbines into insights: A big data approach'. Together they form a unique fingerprint.

Cite this