Combination of minimum entropy deconvolution method and Van Cittert algorithm for features extraction of bearings

Nesrine Gouri; Hocine Bendjama; Mohamed Larbi Mihoub

doi:10.5935/jetia.v11i52.1569

Authors

Nesrine Gouri Mathematical Modeling and Numerical Simulation Research Laboratory, Badji Mokhtar University, Po. Box 12, Annaba 23000, Algeria https://orcid.org/0009-0007-4344-8920 (unauthenticated)
Hocine Bendjama Research Center in Industrial Technologies CRTI, P.O.Box 64, Cheraga Algiers, Algeria https://orcid.org/0000-0001-6221-028X (unauthenticated)
Mohamed Larbi Mihoub Mathematical Modeling and Numerical Simulation Research Laboratory, Badji Mokhtar University, Po. Box 12, Annaba 23000, Algeria https://orcid.org/0000-0001-7230-7165 (unauthenticated)

DOI:

https://doi.org/10.5935/jetia.v11i52.1569

Abstract

Rolling bearings functionality has a primary importance for the correct operation of the rotating machines. In this paper, a monitoring technique based on deconvolution approach is proposed to restore the impulsive shape from the measured vibration signal. This latter is obtained from a convolution of real impulse signal and transmission function. The proposed procedure consists of two major steps; firstly, using the minimum entropy deconvolution (MED) to obtain the inverse filter, secondly introducing the iterative deconvolution algorithm to go back to the initial problem that is mathematically described by the convolution process to restitute the impulsive signal. The proposed procedure is applied to bearing diagnosis, and its effectiveness is validated by simulated and experimental data acquired from operational bearings. Moreover, the monitoring obtained results are satisfactory.