Smart Feature Extraction from Acoustic Camera Multi-sensor Measurements

  • Petia Koprinkova-Hristova
  • Volodymyr Kudriashov
  • Kiril Alexiev
  • Iurii Chyrka
  • Vladislav Ivanov
  • Petko Nedyalkov
Part of the Studies in Computational Intelligence book series (SCI, volume 648)


The paper applies recently developed smart approach for feature extraction from multi-dimensional data sets using Echo state networks (ESN) to the focalized spectra obtained from the acoustic camera multi-sensor measurements. The aim of the study is development of distance diagnostic system for prediction of wearing out of bearings. The procedure for initial features selection and features extraction from the focalized spectra was developed. Then the k-means clustering algorithm and Support vector machine (SVM) classifiers were applied to differentiate the tested bearings into two classes with respect to their condition (“Good” or “Bad”). The results using different dimensions of the extracted features space were compared.


Smart signal processing Multi-sensor system Feature extraction Classification Echo state network IP tuning K-means clustering Support vector machine 



This work was partly supported by the project AComIn, grant 316087, funded by the FP7 Capacity Program (Research Potential of Convergence Regions).


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Petia Koprinkova-Hristova
    • 1
  • Volodymyr Kudriashov
    • 1
  • Kiril Alexiev
    • 1
  • Iurii Chyrka
    • 1
  • Vladislav Ivanov
    • 2
  • Petko Nedyalkov
    • 2
  1. 1.Institute of Information and Communication TechnologiesBulgarian Academy of SciencesSofiaBulgaria
  2. 2.Technical University of SofiaSofiaBulgaria

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