Loading...
Please wait, while we are loading the content...
Similar Documents
Vibration Prediction of Flying IoT Based on LSTM and GRU
| Content Provider | MDPI |
|---|---|
| Author | Hong, Jun-Ki |
| Copyright Year | 2022 |
| Description | 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, |
| Starting Page | 1052 |
| e-ISSN | 20799292 |
| DOI | 10.3390/electronics11071052 |
| Journal | Electronics |
| Issue Number | 7 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-03-27 |
| Access Restriction | Open |
| Subject Keyword | Electronics Industrial Engineering Flying Iot Drone Deep Learning Time Series Vibration Forecasting Long Short-term Memory (lstm) Gated Recurrent Unit (gru) Vibration |
| Content Type | Text |
| Resource Type | Article |
