基于改进CNN-LSTM的开关闩故障诊断方法

高振华; 秦奋起; 王琳琳; 于存贵

doi:10.12382/bgxb.2024.0818

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基于改进CNN-LSTM的开关闩故障诊断方法

更新时间：2025-10-21

- 基于改进CNN-LSTM的开关闩故障诊断方法
- An Improved CNN-LSTM-based Fault Diagnosis Method for Breechblock Opening-closing Mechanism
- 兵工学报 2025年46卷第9期页码：240818
- 作者机构：
  
  1. 南京理工大学机械工程学院, 江苏南京 210094
  2. 中国船舶重工集团公司第七一三研究所, 河南郑州 450015
- 作者简介：
  
  * yu-cungui@njust.edu.cn
- 基金信息：
- DOI：10.12382/bgxb.2024.0818
  中图分类号：
- 收稿：2024-09-09，
  
  网络出版：2025-09-24，
  
  纸质出版：2025-09-30
- 稿件说明：
移动端阅览
高振华, 秦奋起, 王琳琳, 等. 基于改进CNN-LSTM的开关闩故障诊断方法[J]. 兵工学报, 2025,46(9):240818.

Zhenhua GAO, Fenqi QIN, Linlin WANG, et al. An Improved CNN-LSTM-based Fault Diagnosis Method for Breechblock Opening-closing Mechanism[J]. Acta Armamentarii, 2025, 46(9): 240818.
高振华, 秦奋起, 王琳琳, 等. 基于改进CNN-LSTM的开关闩故障诊断方法[J]. 兵工学报, 2025,46(9):240818. DOI： 10.12382/bgxb.2024.0818.

Zhenhua GAO, Fenqi QIN, Linlin WANG, et al. An Improved CNN-LSTM-based Fault Diagnosis Method for Breechblock Opening-closing Mechanism[J]. Acta Armamentarii, 2025, 46(9): 240818. DOI： 10.12382/bgxb.2024.0818.

摘要

针对某舰炮开关闩机构关重件磨损和弹簧弹性减弱两类典型故障模式

传统故障诊断方法主要依赖于人工检查、专家经验推理和理论仿真等方法

不仅时间周期较长

而且诊断精度难以保证。针对此问题

采用深度学习的方法

提出一种基于麻雀搜索算法(Sparrow Search Algorithm

SSA)的格拉姆角场结合卷积神经网络和长短期记忆神经网络(Graham Angle Field-Convolutional Neural Network-Long Short-Term Memory

GAF-CNN-LSTM)的开关闩故障诊断方法。通过试验台架采集开关闩机构故障原始信号并进行预处理

通过时频分析法和格拉姆角场法建立一维时序数据和二维图像故障数据集;将故障数据集分别输入到使用卷积神经网络(Convolutional Neural Network

CNN)和长短期记忆神经网络(Long Short-Term Memory

LSTM)通道中

利用CNN强大的空间特征提取能力和LSTM挖掘数据时序特征能力进行特征提取

并将二者得到的特征信息进行融合

在全连接层和激活函数的作用下输出诊断结果;通过SSA对GAF-CNN-LSTM网络结构中的超参数进行优化

提高模型的诊断精度和适用性。经测试数据验证:提出的SSA-GAF-CNN-LSTM故障诊断模型不仅可以更精准地诊断开关闩机构故障类型

而且具有更强的泛化能力及抗干扰能力

有效地提高了开关闩机构故障诊断的性能。

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.

关键词

Keywords

references

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