基于时序二维化的航空传感器故障检测

Aerial sensor fault detection is of great importance in flight missions. However, the dimensionality of sensor time-series data is extremely high and the time span is extremely long, which lead to poor detection performance of existing methods. To address these problems, this paper proposes a time-series to 2D fault detection (T2D) method for aerial sensor fault detection based on time-series. Firstly, the information entropy is applied to the classification and aggregation approximation algorithm to achieve effective compression of the data while fully retaining the time-series features. Secondly, the gramian angular field is introduced to encode the reduced-dimensional data into two-dimensional images, maintaining the long-range dependence of the original sequence. Finally, a flexible convolution block is designed and inserted into the encoder of the detection network Vision Transformer to improve the detection accuracy of the model. Experimental results show that the T2D model performs significantly better than other models on a simulated time-series dataset of a civilian aircraft test flight, indicating the effectiveness and superiority of the proposed method.