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.
Highlights
In complex systems such as the unmanned aerial vehicles (UAV), the chances of failure are hazardously high
Each data row contains the values of the UAV variables
The One-Class SVM was better in detecting impulse faults in Flight1; its false alarm rate was high for stuck faults in Flight2, and its recall approached zero in the case of the drift in Flight3 and cut faults in Flight4, and this means that One-class SVM failed to detect faults that have a continuous nature
Summary
In complex systems such as the unmanned aerial vehicles (UAV), the chances of failure are hazardously high. Detection algorithms find patterns in data that do not follow an expected behavior. These algorithms are either supervised or unsupervised. These unsuitable commands could result in the crash of the UAV In this example, the altitude command is annotated as the dependent variable, and the altitude sensor as the tested variable. Using relationships of more than two variables in the multivariate dataset might detect the fault It will not permit determining the context of the fault (the dependent variable), because the effect of the different relationships will result in losing track of the context. Each sliding window consists of the previous instances of the dependent variable, and the differential values of the tested one. The trained MDNN platform is used to detect abnormal instances, which could be potential faults. The MDNN algorithm exhibited promising results in all experiments, in which it detected accurately different types of faults, and worked better than the other algorithms while processing the stuck, drift, and cut faults
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