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Comparison of Signal Processing Techniques for Condition Monitoring Based on Artificial Neural Networks

  • M. TiboniEmail author
  • G. Incerti
  • C. Remino
  • M. Lancini
Conference paper
Part of the Applied Condition Monitoring book series (ACM, volume 15)

Abstract

The paper presents the results of a study aimed to compare different signal processing techniques for the condition monitoring of a mechanical system for indexing motion. Artificial feed-forward neural networks (ANN) are used as classifiers. The mechanical system can work in different conditions (variable loads and velocities, lubricant oil with different viscosity) and the ANN identifies the working condition. The monitored variable is the acceleration signal of the rotating table, opportunely pre-processed. The signal processing techniques compared are: Power Spectral Density (PSD), Fast Fourier Transform (FFT), Wavelet, Amplitude Probability Density Function (PDF), Higher Order Spectra (HOS).

Keywords

Condition monitoring Vibrations Signal processing Neural networks 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Mechanical and Industrial Engineering DepartmentUniversità degli Studi di BresciaBresciaItaly

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