基于孪生减元注意力网络的航空发动机故障诊断

In view of the problems that traditional fault diagnosis methods are prone to over-fitting under the condition of insufficient fault samples, and the weak fault features are difficult to be extracted under strong noise conditions, an aero-engine fault diagnosis method based on Siamese reduced-neuron attention networks was proposed. According to the principle of Siamese neural network, pairwise coupling of the samples in the training dataset was conducted, so that the input was changed from samples to sample pairs, and the diversity of input was improved. A reduced-neuron attention mechanism was integrated into the feature extraction module. Among them, the attention mechanism can quickly find useful features through global scanning, and suppress redundant features, which was in good agreement with the situation where the weak gas path fault features of aero-engines were submerged by noise; the reduced-neuron operation can reduce the amount of parameters and alleviate overfitting. The results show that this method achieves an average accuracy of 88.39% on the real monitoring data of CMF56-5B/7B series engines of an airline.