Dimensionality Reduction for the Analysis of Time Series Data from Wind Turbines

Jochen Garcke (), Rodrigo Iza-Teran, Marvin Marks, Mandar Pathare, Dirk Schollbach and Martin Stettner
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Jochen Garcke: Fraunhofer Institute for Algorithms and Scientific Computing SCAI
Rodrigo Iza-Teran: Fraunhofer Institute for Algorithms and Scientific Computing SCAI
Marvin Marks: Fraunhofer Institute for Algorithms and Scientific Computing SCAI
Mandar Pathare: Fraunhofer Institute for Algorithms and Scientific Computing SCAI
Dirk Schollbach: Weidmüller Monitoring Systems GmbH
Martin Stettner: GE Global Research

A chapter in Scientific Computing and Algorithms in Industrial Simulations, 2017, pp 317-339 from Springer

Abstract: Abstract We are addressing two related applications for the analysis of data from wind turbines. First, we consider time series data arising from virtual sensors in numerical simulations as employed during product development, and, second, we investigate sensor data from condition monitoring systems of installed wind turbines. For each application we propose a data analysis procedure based on dimensionality reduction. In the case of virtual product development we develop tools to assist the engineer in the process of analyzing the time series data from large bundles of numerical simulations in regard to similarities or anomalies. For condition monitoring we develop a procedure which detects damages early in the sensor data stream.

Keywords: Wind Turbine; Nonlinear Dimensionality Reduction Approaches; Anomaly Detection; Rotor Blades; Embedding Plots (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-62458-7_16

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DOI: 10.1007/978-3-319-62458-7_16

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