Implementation of Abnormality Diagnosis System for Drone Power Unit by Using Vibration Sensor

Om, Soung-Young; Park, Jihyun; Kim, Yoon-Ho

doi:10.14801/jkiit.2020.18.4.69

Cited by 2 publications

(1 citation statement)

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“…Many studies have been previously conducted to detect the vibrations and abnormalities of motors by deep learning [15][16][17]. In the previous study, a failure diagnostic approach was used to analyze the drone's vibration and predict the abnormality using deep learning to detect the abnormal state of the drone [18][19][20]. Furthermore, studies are conducted to diagnose abnormalities by analyzing and predicting the vibration of a motor using LSTM [21][22][23][24][25][26][27][28][29] and GRU [30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Vibration Prediction of Flying IoT Based on LSTM and GRU

Hong

2022

Electronics

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Drones, flying Internet of Things (IoT), have been widely used in several industrial fields, including rescue, delivery, military, and agriculture. Motors connected to a drone’s propellers play a crucial role in its movement. However, once the motor is damaged, the drone is at risk of falling. Thus, to prevent the drone from falling, an accurate and reliable prediction of motor vibration is necessary. In this study, four types of time series vibration data collected in the time domain from motors are predicted using long short-term memory (LSTM) and gated recurrent unit (GRU), and the accuracy and time efficiency of the predicted and actual vibration waveforms are compared and examined. According to the simulation results, the coefficient of determination, R2, and the root mean square error values obtained from the actual and predicted vibrations by the LSTM and GRU are similar. Furthermore, both the LSTM and GRU show excellent performance in forecasting future motor vibration, but GRU can predict future vibration about 22.79% faster than LSTM.

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