This paper proposes an automatic method, for monitoring inverter fed induction motors using external stray flux measurements. The method relies on the marginal power spectrum of the Synchrosqueezed Wavelet Transform for the feature extraction stage and on Principal Component Analysis for the reduction of the high dimensionality of the generated feature vector. For the next stage two approaches were tested: a) a fault detector based on a one-class classifier and b) a fault diagnosis module based on a multiclass classifier. Both of them achieve high accuracies when tested with measurements coming from an experimental set up able to simulate stator short circuits and bearing faults. An explanation of the performance is given by visual inspection of the projection of the feature vectors into a three-dimensional space.
An Automatic Method for Condition Monitoring of Inverter Fed Induction Motors
L. Frosini
;A. Albini
2018-01-01
Abstract
This paper proposes an automatic method, for monitoring inverter fed induction motors using external stray flux measurements. The method relies on the marginal power spectrum of the Synchrosqueezed Wavelet Transform for the feature extraction stage and on Principal Component Analysis for the reduction of the high dimensionality of the generated feature vector. For the next stage two approaches were tested: a) a fault detector based on a one-class classifier and b) a fault diagnosis module based on a multiclass classifier. Both of them achieve high accuracies when tested with measurements coming from an experimental set up able to simulate stator short circuits and bearing faults. An explanation of the performance is given by visual inspection of the projection of the feature vectors into a three-dimensional space.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
