Abstract:

In view of the two typical failure modes of breechblock opening-closing mechanism for a naval gun,namely the wear of the key parts and the weakening of spring elasticity,the traditional fault diagnosis methods mainly rely on manual inspection,expert empirical reasoning and theoretical simulation.However,these methods not only take a long time for diagnosis,but also the diagnostic accuracy cannot be guarantee.In order to solve this problem,a fault diagnosis method of Gram angle field combined with convolutional neural network and long short-term memory neural network (GAF-CNN-LSTM) based on sparrow search algorithm (SSA) is proposed by using the deep learning algorithms.Firstly,the original fault signal of the breechblock opening-closing mechanism is collected and preprocessed by the test bench,and the one-dimensional time-series data and two-dimensional image fault dataset are established by the time-frequency analysis method and the Gramian angular field method.Then the fault dataset is input into the LSTM and CNN channels,respectively,and the powerful spatial feature extraction ability of CNN and the time-series feature ability of LSTM mining data are used to extract the features,and the feature informations obtained by the two abilities are fused to output the diagnostic results under the action of the fully connected layer and activation function.Finally,the SSA optimization algorithm is used to optimize the hyperparameters in the GAF-CNN-LSTM network structure to improve the diagnostic accuracy and applicability of the model.The proposed SSA-GAF-CNN-LSTM fault diagnosis model is verified by the test data.The result shows that the proposed fault diagnosis model can not only diagnose the fault type of the breechblock opening-closing mechanism for naval gun more accurately,but also has stronger generalization ability and anti-interference ability,which effectively improves the fault diagnosis performance of the breechblock opening-closing mechanism.

Key words: fault diagnosis, breechblock opening-closing mechanism, deep learning algorithm, swarm intelligence optimization algorithm, Gramian angular field