Classifier ensembles to improve the robustness to noise of bearing fault diagnosis

@article{Lazzerini2011ClassifierET,
  title={Classifier ensembles to improve the robustness to noise of bearing fault diagnosis},
  author={Beatrice Lazzerini and Sara Lioba Volpi},
  journal={Pattern Analysis and Applications},
  year={2011},
  volume={16},
  pages={235-251}
}
In this paper, we perform a noise analysis to assess the degree of robustness to noise of a neural classifier aimed at performing multi-class diagnosis of rolling element bearings. We work on vibration signals collected by means of two accelerometers and we consider ten levels of noise, each of which characterized by a different signal-to-noise ratio ranging from 40.55 to −11.35 db. We classify the noisy signals by means of a neural classifier initially trained on signals without noise and then… CONTINUE READING