关键词: 故障诊断, 开关闩机构, 深度学习算法, 群体智能优化算法, 格拉姆角场

Abstract: In order to solve the problems of complex and changeable working conditions and harsh working environment of a Breechblock Opening-closing Mechanism of naval gun, which lead to the difficulty of fault type detection, a Breechblock Opening-closing Mechanism fault diagnosis method of Gramian angular field combined with convolutional neural network and long short-term memory neural network (GAF-CNN-LSTM) based on sparrow optimization algorithm (SSA) was proposed. Firstly, the original signal of the Breechblock Opening-closing Mechanism fault was collected and preprocessed, and the one-dimensional time series data and two-dimensional image fault dataset were established by the time-frequency analysis method and the Gramian angular field method; Then, the fault dataset was 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 were used to extract features, and the feature information obtained by the two was 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. Verified by the test data: the proposed SSA-GAF-CNN-LSTM fault diagnosis model can not only diagnose the fault type of the Breechblock Opening-closing Mechanism of 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 algorithms, swarm intelligence optimization algorithm, Gramian angular field