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Article Dans Une Revue IEEE Transactions on Energy Conversion Année : 2021

Induction Machine Fault Detection and Classification Using Non-Parametric, Statistical-Frequency Features and Shallow Neural Networks

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Rahul R. Kumar
  • Fonction : Auteur
Andrea Tortella
  • Fonction : Auteur
Mauro Andriollo
  • Fonction : Auteur

Résumé

This article presents a two-stage fault detection and classification scheme specifically designed for rotating electrical machines. The approach involves the use of new condition indicators that are specific to the frequency domain. The paper proposes two distinct features: one based on the extraction of peaks by using the prominence measure, a technique originating from the topology of mountains, and other based on the calculation of the occupied band power ratio for specific characteristic fault frequencies. A linear based feature reduction technique, the principal component analysis (PCA) has been employed to represent all the data. Afterwards, shallow neural networks have been used to detect and classify the three-phase current signals online. The effectiveness of the proposed scheme has been validated experimentally by using signals obtained with grid and inverter fed induction motors.
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Dates et versions

hal-03631428 , version 1 (05-04-2022)

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Citer

Rahul R. Kumar, Giansalvo Cirrincione, Maurizio Cirrincione, Andrea Tortella, Mauro Andriollo. Induction Machine Fault Detection and Classification Using Non-Parametric, Statistical-Frequency Features and Shallow Neural Networks. IEEE Transactions on Energy Conversion, 2021, 36 (2), pp.1070-1080. ⟨10.1109/TEC.2020.3032532⟩. ⟨hal-03631428⟩

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