28 March 2017
EACS 2016 paper - SENSOR SELECTION BASED ON PRINCIPAL COMPONENT ANALYSIS FOR FAULT DETECTION IN WIND TURBINES
F. Pozo, Y. Vidal, et al.
EACS 2016 Paper No. 175Growing interest for improving the reliability of safety-critical structures, such as wind turbines, has led to the advancement of structural health monitoring (SHM). Existing techniques for fault detection can be broadly classified into two major categories: model-based methods and signal processing-based methods. This work focuses in the signal-processing-based fault detection by using principal component analysis (PCA) as a way to condense and extract information from the collected signals. In particular, the goal of this work is to select a reduced number of sensors to be used. From a practical point of view, a reduced number of sensors installed in the structure leads to a reduced cost of installation and maintenance. Besides, from a computational point of view,...
Mechanical engineering not elsewhere classified
principal component analysis