Data-based localisation methods using simulated data with application to small-scale structures

Abstract

Several methods to localise sources of vibrations have been established in the literature. A great amount of those methods are based on databases with features of known impact positions. Great effort needs to be put into highly expensive experiments that deliver those databases. In this paper, we propose several simulation techniques that may replace the expensive experiments for source localisation. The paper compares the localisation accuracy of simulated and experimental data for two different localisation approaches, the reference database method and neural networks. Both methods process signal arrival time differences from several positions on the structure. The methods are exemplarily applied to a complex small-scale structure from the automotive industry: The small dimensions of the brake disk hat and the inclusion of holes is a challenging task for the accuracy of the applied localisation techniques. Results show that simulated data can replace experimentally gained data well in case of the reference database method, whereas the neuronal networks approach should stick to experimentally gained data. The evaluations show that, despite the small dimension, the relative localisation accuracy is within accepted ranges of literature.