基于AC-CNN的航空发动机气路故障诊断算法OA

To address the challenges of insufficient fault information extraction and low diagnostic accuracy in aero-engine gas path component fault diagnosis,an improved fault diagnosis model based on an AC-CNN is proposed,featur-ing a diagnostic mode of offline training and online diagnosis is proposed.First,the CBAM is optimized through chan-nel reduction and shared feature transformation,and embedded into a convolutional neural network.Then depthwise separable convolution is introduced to further reduce computational complexity,constructing an AC-CNN backbone for feature extraction.This design strengthens the model's feature representation capability and receptive field,thereby im-proving diagnostic accuracy.Finally,the refined high-quality features are fed into an ELM classifier to achieve fault classification.Experimental results show that the proposed model improves diagnostic accuracy to 99.29％while avoid-ing an excessive increase in computational complexity,requiring only 38.12％of the computational load of the tradi-tional CBAM module.The ELM classifier improves generalization and reduces overfitting.Compared to other models,AC-CNN shows superior robustness and is better suited for aero-engine gas path fault diagnosis.