摘要
当前基于深度学习的故障识别方法普遍面临高数据依赖性、高昂计算成本与时间开销,以及模型泛化能力受限等挑战.为此,本研究提出一种融合MobileNetV3、选择性核网络(selective kernel network,SKNet)及堆叠长短期记忆网络(stacked long short-term memory network,Stacked LSTM)的轻量化高精度故障识别模型.首先进行输入数据预处理,将处理后的数据转换成适应卷积层的输入格式.在特征提取阶段,利用改进的MobileNetV3骨干网络进行深度特征挖掘,其倒置残差模块在保留深度可分离卷积高效性的基础上,策略性地嵌入SE(squeeze-andexcitation)与SK(selective kernel)双重注意力机制,有效兼顾通道信息交互与多尺度特征自适应选择,显著提升了特征表征能力并降低了计算复杂度.随后,堆叠LSTM捕获振动信号中的长距离时序依赖关系.最终通过全连接层实现特征压缩与分类决策,构建端到端识别系统.实验结果显示,本文模型识别准确率达到99.47%,与传统的齿轮箱故障识别技术相比,该方法在识别精准度和模型泛化能力方面均呈现出显著优势.
Current fault identification methods based on deep learning generally face challenges such as high data dependency,high computational cost and time consumption,and limited model generalization ability.To address these issues,this study proposes a lightweight and high-precision fault identification model that integrates the MobileNetV3,selective kernel network(SKNet),and stacked long short-term memory network(Stacked LSTM).First,the input data is preprocessed,and the processed data is converted into a format suitable for convolutional layer input.In the feature extraction stage,an improved MobileNetV3 backbone network is employed for deep feature mining.On the basis of retaining the efficiency of depthwise separable convolution,an inverted residual module strategically embeds a dual attention mechanism combining squeeze-and-excitation(SE)and selective kernel(SK),effectively enhancing channel information interaction and multi-scale feature adaptive selection,thus significantly improving feature representation and reducing computational complexity.Subsequently,the stacked LSTM captures long-term temporal dependencies in the vibration signals.Finally,feature compression and classification decision-making are achieved through the fully connected layer,thus constructing an end-to-end recognition system.Experimental results show that the recognition accuracy of the proposed model reaches 99.47%,demonstrating significant advantages in recognition accuracy and model generalization ability compared with traditional gearbox fault identification techniques.
作者
杨辰峰
杨喜旺
黄晋英
范振芳
刘晶晶
YANG Chen-Feng;YANG Xi-Wang;HUANG Jin-Ying;FAN Zhen-Fang;LIU Jing-Jing(School of Computer Science and Technology,North University of China,Taiyuan 030051,China;School of Information and Communication Engineering,Shanxi University of Electronic Science and Technology,Linfen 041000,China;School of Mechanical Engineering,North University of China,Taiyuan 030051,China)
出处
《计算机系统应用》
2026年第1期237-245,共9页
Computer Systems & Applications
基金
山西省基础研究计划(202203021211096)。
关键词
故障识别
深度学习
MobileNetV3
选择性核网络
堆叠长短期记忆网络
fault identification
deep learning
MobileNetV3
selective kernel network(SKNet)
stacked long short-term memory network(Stacked LSTM)
