Next Article in Journal
Thermal Diffusion in Fibrous Aerogel Blankets
Previous Article in Journal
Effects of Temperature on the Flow and Heat Transfer in Gel Fuels: A Numerical Study
Open AccessArticle

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

Department of power systems, Kaunas University of Technology, LT-51367 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
Energies 2020, 13(4), 820; https://doi.org/10.3390/en13040820
Received: 16 January 2020 / Revised: 7 February 2020 / Accepted: 10 February 2020 / Published: 13 February 2020
(This article belongs to the Section Electrical Power and Energy System)
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 brushless DC motor; stator; vibrations; classification; long short-term memory networks; deep networks; neural networks
MDPI and ACS Style

Zimnickas, T.; Vanagas, J.; Dambrauskas, K.; Kalvaitis, A.; Ažubalis, M. Application of Advanced Vibration Monitoring Systems and Long Short-Term Memory Networks for Brushless DC Motor Stator Fault Monitoring and Classification. Energies 2020, 13, 820.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop