This paper presents a multi-sensor approach for the bearings diagnostics of induction motors. The physical quantities taken into account are vibrations (acceleration signals), stator current, axial and radial external leakage fluxes. The fault to detect is a “cyclic” localized crack on the external ring of the drive-end bearing. At first, piezoelectric and MEMS accelerometers are compared, showing that a cheap sensor could give sufficient diagnostic information. Subsequently, the analysis of the electromagnetic signals’ spectra is used to detect the fault. In order to facilitate this analysis, the exact characteristic fault frequency is obtained from the vibration spectra and identified in the current and flux signatures. This method avoids the slippage uncertainty on the electromagnetic spectra’s fault peaks detection. Further tests with oscillation load torque show new signatures in the spectra and tests with inverter highlight the peculiar issues coming from this supply condition.

(2020). A Multisensor Induction Motors Diagnostics Method for Bearing Cyclic Fault . Retrieved from https://hdl.handle.net/10446/263813

A Multisensor Induction Motors Diagnostics Method for Bearing Cyclic Fault

Minervini, Marcello;
2020-01-01

Abstract

This paper presents a multi-sensor approach for the bearings diagnostics of induction motors. The physical quantities taken into account are vibrations (acceleration signals), stator current, axial and radial external leakage fluxes. The fault to detect is a “cyclic” localized crack on the external ring of the drive-end bearing. At first, piezoelectric and MEMS accelerometers are compared, showing that a cheap sensor could give sufficient diagnostic information. Subsequently, the analysis of the electromagnetic signals’ spectra is used to detect the fault. In order to facilitate this analysis, the exact characteristic fault frequency is obtained from the vibration spectra and identified in the current and flux signatures. This method avoids the slippage uncertainty on the electromagnetic spectra’s fault peaks detection. Further tests with oscillation load torque show new signatures in the spectra and tests with inverter highlight the peculiar issues coming from this supply condition.
2020
Minervini, Marcello; Frosini, Lucia; Hasani, Leutrim; Albini, Andrea
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/263813
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