This paper presents a fault diagnosis and classification scheme for induction machines by using motor current signature analysis together with neural networks. The adopted strategy utilizes three-phase stator current sensors and calculates appropriate features using non-parametric and a statistical approach. The feature-set is reduced by means of the principal component analysis which acts as a pre-processor for the multilayer perceptron neural network. This two stage classification is carried out for detection and classification of faults. The efficacy of the proposed scheme is validated experimentally by using grid and inverter fed induction motors.

Accurate Fault Diagnosis and Classification Scheme Based on Non-Parametric, Statistical-Frequency Features and Neural Networks

Kumar, R. R.;CIRRINCIONE, GIANSALVO;Andriollo, M.;Tortella, A.
2018

Abstract

This paper presents a fault diagnosis and classification scheme for induction machines by using motor current signature analysis together with neural networks. The adopted strategy utilizes three-phase stator current sensors and calculates appropriate features using non-parametric and a statistical approach. The feature-set is reduced by means of the principal component analysis which acts as a pre-processor for the multilayer perceptron neural network. This two stage classification is carried out for detection and classification of faults. The efficacy of the proposed scheme is validated experimentally by using grid and inverter fed induction motors.
2018
Conference Proceedings
978-1-5386-2477-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3282774
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