Comparative Performance Analysis of RNN Techniques for Predicting Concatenated Normal and Abnormal Vibrations

Abstract

We analyze the comparative performance of predicting the transition from normal to abnormal vibration states, simulating the motor’s condition before a drone crash, by proposing a concatenated vibration prediction model (CVPM) based on recurrent neural network (RNN) techniques. Subsequently, using the proposed CVPM, the prediction performances of six RNN techniques: long short-term memory (LSTM), attention-LSTM (Attn.-LSTM), bidirectional-LSTM (Bi-LSTM), gate recurrent unit (GRU), attention-GRU (Attn.-GRU), and bidirectional-GRU (Bi-GRU), are analyzed comparatively. In order to assess the prediction accuracy of these RNN techniques in predicting concatenated vibrations, both normal and abnormal vibration data are collected from the motors connected to the drone’s propellers. Consequently, a concatenated vibration dataset is generated by combining 50% of normal vibration data with 50% of abnormal vibration data. This dataset is then used to compare and analyze vibration prediction performance and simulation runtime across the six RNN techniques. The goal of this analysis is to comparatively analyze the performances of the six RNN techniques for vibration prediction. According to the simulation results, it is observed that Attn.-LSTM and Attn.-GRU, incorporating the attention mechanism technique to focus on information highly relevant to the prediction target through unidirectional learning, demonstrate the most promising predictive performance among the six RNN techniques. This implies that employing the attention mechanism enhances the concentration of relevant information, resulting in superior predictive accuracy compared to the other RNN techniques.