The electrical performance test of aerospace products is to test the signal feedback of different parts of aerospace products with external excitation， so as to determine the quality of each component. It is one of the important means to test the quality of aerospace products. However， the signal obtained from the test has the characteristics of many channels， long timing and complexity， and it is difficult to rely on traditional methods to process the signal. At present， the processing method adopts manual statistics， which is inefficient and error prone. It is urgent to improve the processing efficiency and accuracy with the help of deep neural network. In this paper， the Siamese neural network is introduced in electrical performance test of aerospace products， and the data information is resolved into image information and transformed into image recognition problem. Several backbone network models are compared and analyzed， and ResNet50 is finally selected as the backbone network. The experimental results show that under the same experimental conditions， the algorithm proposed can effectively identify the waveform information of 78 channel signals and the recognition accuracy reaches 90.385%， improving the recognition ability and intelligent level of complex test signals of aerospace products.
WANG Yufei
,
LIU Xiaojia
,
LIU Huan
,
CAO Lijun
,
LUO Zhiqiang
. Application of Siamese neural network in electrical performance test of aerospace products[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022
, 43(S1)
: 727048
-727048
.
DOI: 10.7527/S1000-6893.2022.27048
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