Higher-Order Statistics applied to machine learning as an approach to identify broken rotor bars in induction motors publicado no periódico IEEE Latin America Transactions 

Higher-Order Statistics applied to machine learning as an approach to identify broken rotor bars in induction motors publicado no periódico IEEE Latin America Transactions 

Parabéns aos autores colaboradores do LAPISCO pelo artigo “Higher-Order Statistics applied to machine learning as an approach to identify broken rotor bars in induction motors” publicado no periódico IEEE Latin America Transactions

Abstract: Induction motors are reported as the horse power in industries. Due to its importance, researchers have been studied how to predict its faults in order to improve reliability. Condition health monitoring plays an important role in this field, since it is possible to predict failures by analyzing its operational data. This paper proposes the usage of vibration signals, combined with Higher-Order Statistics (HOS) and machine learning methods to detect broken bars in a squirrel-cage three-phase induction motor. The Multi-Layer Perceptron and Optimum-Path Forest have presented as promising approaches for faults classifications in an induction motor.

Paper at IEEE Xplore: https://ieeexplore.ieee.org/document/8528245 

Paper at USP directory (free): http://www.ewh.ieee.org/reg/9/etrans/ieee/issues/vol16/vol16issue08Aug.2018/28Sanmartin.htm 

Deixe uma resposta

O seu endereço de e-mail não será publicado. Campos obrigatórios são marcados com *