A Fault Detection Method Based on Enhanced GRU
Bo Chen, Peng Yu, Binbin Gu, Yue Gang Luo, Datong Liu · 2021
Fault detection has been deployed in many cases. It will help improve the stability of the system. Data-driven methods can provide credible evidence for fault detection. For time series which may include a lot of noise, the performance of typical methods may be affected. This article raises an enhanced gate recurrent unit (GRU) method to analyze unmanned aerial vehicle (UAV) flight data that are affected by the vibration of motors or wind. Firstly, the raw data are denoised and normalized to improve the effect of the analysis. Secondly, a gate recurrent unit (GRU) model is built to estimate one of the sensor data based on others. Finally, to detect fault data, the method based on residuals and threshold is applied. To evaluate the effectiveness of the method, the simulation data of UAV are applied to the method, and it can be found that the proposed method is effective in fault detection.