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| Diesel Engine Fault Diagnosis Method Based on Improved Residual Convolution Network |
| DOI：10.13949/j.cnki.nrjgc.2023.05.009 |
| Key Words:diesel engine fault diagnosis convolutional neural network（CNN） residual module |
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| Abstract:Aiming at the problem that the diesel engine fault diagnosis method based on convolutional neural network（CNN） tended to over-fit and the diagnatic accuracy was low when the samples were scarce， an “end-to-end” diesel engine fault diagnosis method based on an improved residual convolution network was proposed. The continuously differentiable exponential linear units（CELU） were used as the activation function of CNN and a small batch training method was adopted to improve the ability of feature extraction and accelerate model convergence. The residual module was added to the model to integrate the abstract features extracted from the deep network with the surface features， avoiding the loss of feature information and gradient loss caused by the deep network. The diesel engine fault simulation experiment shows that the method can achieve 95.5% fault diagnosis accuracy with only 20 samples used for model training. Compared with CNN， the method can significantly improve the accuracy of fault diagnosis under different types and scales of training sets. |
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