Detecting Incipient Faults in Quad-rotor Unmanned Aerial Vehicle Based on Detrending and Denoising Techniques

Incipient faults are not easy to be detected, because they tend to be buried by the trend or the measurement noise. The paper proposes an applicable method for detecting incipient fault in the quad-rotor unmanned aerial vehicle (UAV). The approach in this paper is based on a detrending and denoising technique. The detrending algorithm is implemented based on the selected design functions, which can extract the normal trend from the training data, and then predict the normal trend in the testing data. The denoising algorithm is realized based on the weighted cumulative sum method, which can reduce the variance of the noise in the prediction residual. The proposed method is applied to detect the incipient fault in an experimental quad-rotor UAV, which shows that the performance of the proposed method is better than the traditional multivariate detection statistic in detecting incipient faults.