Fuzzy diagnostics for gearbox failures based on induction motor current and wavelet entropy

In this work, a strategy is proposed for the automatic diagnosis of failures in gearboxes using current signals from an induction motor. The stator currents were represented by the extended Park vector approach technique and decomposed by the wavelet packet transform. The calculation of the wavelet...

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Bibliographic Details
Published inJournal of the Brazilian Society of Mechanical Sciences and Engineering Vol. 43; no. 5
Main Authors de Sena, Alexander Patrick Chaves, de Freitas, Isaac Soares, Filho, Abel Cavalcante Lima, Sobrinho, Carlos Alberto Nobrega
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2021
Springer Nature B.V
Subjects
Diagnosis
Engineering
Entropy
Failure
Gearboxes
Induction motors
Mechanical Engineering
Review
Rotation
Wavelet transforms
Broken tooth
Fuzzy inference
Gears
Wavelet entropy
Surface wear
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Summary:In this work, a strategy is proposed for the automatic diagnosis of failures in gearboxes using current signals from an induction motor. The stator currents were represented by the extended Park vector approach technique and decomposed by the wavelet packet transform. The calculation of the wavelet packet entropy promoted the distinction between broken tooth failures and the levels of severity of surface wear. The entropies of two wavelet details were used as inputs for a fuzzy inference for automatic classification of the gearbox condition. The experimental results presented a high rate of correctness in the fuzzy diagnosis, confirming the efficiency of the strategy for high rotation (60 Hz) and low rotation (20 Hz). The strategy presents simplicities related to the practical implementation and reduction of the amount of data analyzed.
ISSN:1678-5878
1806-3691
DOI:10.1007/s40430-021-02964-z