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).

Comparison of signal processing techniques for condition monitoring based on artificial neural networks

Tiboni, M.
;
Incerti, G.;Remino, C.;Lancini, M.
2019-01-01

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).
2019
978-1-4939-9050-4
978-1-4939-9051-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11379/515527
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