Application of Advanced Vibration Monitoring Systems and Long Short-Term Memory Networks for Brushless DC Motor Stator Fault Monitoring and Classification

Tomas Zimnickas, Jonas Vanagas, Karolis Dambrauskas, Artūras Kalvaitis and Mindaugas Ažubalis
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Tomas Zimnickas: Department of Power Systems, Kaunas University of Technology, LT-51367 Kaunas, Lithuania
Jonas Vanagas: Department of Power Systems, Kaunas University of Technology, LT-51367 Kaunas, Lithuania
Karolis Dambrauskas: Department of Power Systems, Kaunas University of Technology, LT-51367 Kaunas, Lithuania
Artūras Kalvaitis: Department of Power Systems, Kaunas University of Technology, LT-51367 Kaunas, Lithuania
Mindaugas Ažubalis: Department of Power Systems, Kaunas University of Technology, LT-51367 Kaunas, Lithuania

Energies, 2020, vol. 13, issue 4, 1-18

Abstract: In this research, electric motors faults and their identification is reviewed. Brushless direct-current (BLDC) motors stator fault identification using long short-term memory neural networks were analyzed. A proposed method of vibration data acquisition using cloud technologies with high accuracy, feature extraction using spectral entropy, and instantaneous frequency and standardization using mean and standard deviation was reviewed. Additionally, model training with raw and standardized data was compared. A total model accuracy of 97.10 percent was achieved. The proposed methods could successfully identify the motor stator status from normal, to loss of stator winding imminent and arcing, and lastly to open circuit in stator winding—motor needing to stop immediately—by using gathered data from real experiments, training the model and testing it theoretically.

Keywords: brushless DC motor; stator; vibrations; classification; long short-term memory networks; deep networks; neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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