Using Multiple Deep Neural Networks Platform to Detect Different Types of Potential Faults in Unmanned Aerial Vehicles

Abstract

Many researchers developed new algorithms to predict the faults of unmanned aerial vehicles (UAV). These algorithms detect anomalies in the streamed data of the UAV and label them as potential faults. Most of these algorithms consider neither the complex relationships among the UAV variables nor the temporal patterns of the previous instances, which leaves a potential opportunity for new ideas. A new method for analyzing the relationships and the temporal patterns of every two variables to detect the potentially defected sensors. The proposed method depends on a new platform, which is composed of multiple deep neural networks. The method starts by building and training this platform. The training step requires reshaping the dataset into a set of subdatasets. Each new subdataset is used to train one deep neural network. In the testing phase, the method reads new instances of the UAV testing dataset. The output of the algorithm is the predicted potential faults. The proposed approach is evaluated and compared it with other well-known algorithms. The proposed approach showed promising results in predicting different kinds of faults.