This paper deals with the diagnosis of faults in roller-element bearings as the core of a dedicated Condition based Maintenance (CBM) system. Vibration signals recorded by accelerometers feed into a classification model in charge of monitoring and evaluating bearing wear. The chosen feature extraction technique is based on the computation of the Discrete Fourier Transform (DFT) and on the estimation of the normalized frequency content in each of the considered spectrum sub-bands as an indicative measure of the state of health of the rollerelement bearing. Three different damage attributes have been investigated. For each attribute, both Support Vector Machines (SVM) and neurofuzzy Min-Max classifiers have been employed as the core of the diagnostic system. Test results show that it is possible to achieve high accuracy in all diagnostic problems considered. The pre-processing procedure and the classification stage, especially in the case of Min-Max fuzzy networks, do not require demandi

Multi-fault diagnosis of rolling-element bearings in electric machines / DEL VESCOVO, Guido; Paschero, Maurizio; Rizzi, Antonello; Roberto Di, Salvo; FRATTALE MASCIOLI, Fabio Massimo. - STAMPA. - (2010), pp. 1-6. ((Intervento presentato al convegno 19th International Conference on Electrical Machines, ICEM 2010 tenutosi a Rome; Italy nel 6 September 2010 through 8 September 2010 [10.1109/icelmach.2010.5608123].

Multi-fault diagnosis of rolling-element bearings in electric machines

DEL VESCOVO, Guido;PASCHERO, Maurizio;RIZZI, Antonello;FRATTALE MASCIOLI, Fabio Massimo
2010

Abstract

This paper deals with the diagnosis of faults in roller-element bearings as the core of a dedicated Condition based Maintenance (CBM) system. Vibration signals recorded by accelerometers feed into a classification model in charge of monitoring and evaluating bearing wear. The chosen feature extraction technique is based on the computation of the Discrete Fourier Transform (DFT) and on the estimation of the normalized frequency content in each of the considered spectrum sub-bands as an indicative measure of the state of health of the rollerelement bearing. Three different damage attributes have been investigated. For each attribute, both Support Vector Machines (SVM) and neurofuzzy Min-Max classifiers have been employed as the core of the diagnostic system. Test results show that it is possible to achieve high accuracy in all diagnostic problems considered. The pre-processing procedure and the classification stage, especially in the case of Min-Max fuzzy networks, do not require demandi
19th International Conference on Electrical Machines, ICEM 2010
Bearing wear; Classification models; Condition-based maintenance
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Multi-fault diagnosis of rolling-element bearings in electric machines / DEL VESCOVO, Guido; Paschero, Maurizio; Rizzi, Antonello; Roberto Di, Salvo; FRATTALE MASCIOLI, Fabio Massimo. - STAMPA. - (2010), pp. 1-6. ((Intervento presentato al convegno 19th International Conference on Electrical Machines, ICEM 2010 tenutosi a Rome; Italy nel 6 September 2010 through 8 September 2010 [10.1109/icelmach.2010.5608123].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/62814
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