Vibration analysis for the classification of damage motor PT Petrokimia Gresik using fast fourier transform and neural network

Musthofa, Arif; Asfani, Dimas Anton; Negara, I Made Yulistya; Fahmi, Daniar; Priatama, Nirma

doi:10.1109/isitia.2016.7828690

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…Main contributions of this paper are as follows: Fast and very detailed results with advanced and sensitive vibration sensors combined with a data-gathering device with a high sampling rate and cloud technologies used, compared to other research that measured vibrations, for example [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%