Visual Mining of Industrial Gas Turbines Sensor Data as an Industry 4.0 Application

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.