Abstract
Industrial gas turbines for power generation are advanced engines that require constant and detailed monitorization using internal and external sensors. These sensors generate a large flow of data in the form of multivariate time series that are amenable to analysis using pattern recognition methods with the objective of improving and optimizing turbine operation. One aspect this may take is visual analytics, where dimensionality reduction methods can be used to intuitively visualize the multivariate time series. This brief paper provides a proof of concept and some case scenarios of a visual turbine-monitorization tool, based on the UMAP method, combined with clustering using HDBSCAN and unsupervised Agnostic Feature Selection. It can be considered as a first step towards a data-centered approach to gas turbine management within the industry 4.0 framework, based on the mining of turbine sensor data.
This research was funded by Siemens Energy Industrial Applications Division. We are particularly grateful to D. Naderi and E. Bahilo from the Data Analytics department in Finspang, O. Pozo and A. Celano from the DigiHub Barcelona, E. Joelsson from the Remote Diagnostic Center and the Data Platform team for their help and technical expertise. We also thank M. De Castro for information concerning types of turbine deterioration.
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Astudillo Aguilar, A.X., Rosso, S., Gibert, K., Vellido, A. (2022). Visual Mining of Industrial Gas Turbines Sensor Data as an Industry 4.0 Application. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_10
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