Rolling element bearings are one of the most important component in every rotating machinery. As a result, their diagnosis before occurrence of any catastrophic failure is of vital importance and vibration based diagnosis is very popular approach. In this paper, the performance of a recently proposed method, Autogram, will be investigated on different data sets provided by Politecnico di Torino and University of Cincinnati. The results will be compared with other well-established methods such as Fast Kurtogram and Spectral Correlation.

Analysis of autogram performance for rolling element bearing diagnosis by using different data sets / Moshrefzadeh, A.; Fasana, A.; Garibaldi, L.. - STAMPA. - 15:(2019), pp. 132-141. ((Intervento presentato al convegno International Conference on Condition Monitoring of Machinery in Non-Stationary Operation tenutosi a Santander (Spagna) nel 20-22 June [10.1007/978-3-030-11220-2_14].

Analysis of autogram performance for rolling element bearing diagnosis by using different data sets

Moshrefzadeh A.;Fasana A.;Garibaldi L.
2019

Abstract

Rolling element bearings are one of the most important component in every rotating machinery. As a result, their diagnosis before occurrence of any catastrophic failure is of vital importance and vibration based diagnosis is very popular approach. In this paper, the performance of a recently proposed method, Autogram, will be investigated on different data sets provided by Politecnico di Torino and University of Cincinnati. The results will be compared with other well-established methods such as Fast Kurtogram and Spectral Correlation.
978-3-030-11219-6
978-3-030-11220-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2817787