Comparative Performance Analysis of Vibration Prediction Using RNN Techniques

Abstract

Drones are increasingly used in several industries, including rescue, firefighting, and agriculture. If the motor connected to a drone’s propeller is damaged, there is a risk of a drone crash. Therefore, to prevent such incidents, an accurate and quick prediction tool of the motor vibrations in drones is required. In this study, normal and abnormal vibration data were collected from the motor connected to the propeller of a drone. The period and amplitude of the vibrations are consistent in normal vibrations, whereas they are irregular in abnormal vibrations. The collected vibration data were used to train six recurrent neural network (RNN) techniques: long short-term memory (LSTM), attention-LSTM (Attn.-LSTM), bidirectional-LSTM (Bi-LSTM), gated recurrent unit (GRU), attention-GRU (Attn.-GRU), and bidirectional GRU (Bi-GRU). Then, the simulation runtime it took for each RNN technique to predict the vibrations and the accuracy of the predicted vibrations were analyzed to compare the performances of the RNN model. Based on the simulation results, the Attn.-LSTM and Attn.-GRU techniques, incorporating the attention mechanism, had the best efficiency compared to the conventional LSTM and GRU techniques, respectively. The attention mechanism calculates the similarity between the input value and the to-be-predicted value in advance and reflects the similarity in the prediction.