Liu Yanyan, Gao Tongxin, Wu Wenxu, Sun Yongquan
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Institute of Sensor and Reliability Engineering, Harbin University of Science and Technology, Harbin 150080, China.
Sensors (Basel). 2024 Nov 26;24(23):7540. doi: 10.3390/s24237540.
The working conditions of planetary gearboxes are complex, and their structural couplings are strong, leading to low reliability. Traditional deep neural networks often struggle with feature learning in noisy environments, and their reliance on one-dimensional signals as input fails to capture the interrelationships between data points. To address these challenges, we proposed a fault diagnosis method for planetary gearboxes that integrates Markov transition fields (MTFs) and a residual attention mechanism. The MTF was employed to encode one-dimensional signals into feature maps, which were then fed into a residual networks (ResNet) architecture. To enhance the network's ability to focus on important features, we embedded the squeeze-and-excitation (SE) channel attention mechanism into the ResNet34 network, creating a SE-ResNet model. This model was trained to effectively extract and classify features. The developed method was validated using a specific dataset and achieved an accuracy of about 98.1%. The results demonstrate the effectiveness and reliability of the developed method in diagnosing faults in planetary gearboxes under strong noise conditions.
行星齿轮箱的工作条件复杂，其结构耦合性强，导致可靠性较低。传统的深度神经网络在噪声环境中进行特征学习时往往存在困难，并且它们依赖一维信号作为输入，无法捕捉数据点之间的相互关系。为应对这些挑战，我们提出了一种用于行星齿轮箱的故障诊断方法，该方法集成了马尔可夫转移场（MTF）和残差注意力机制。MTF用于将一维信号编码为特征图，然后将其输入到残差网络（ResNet）架构中。为增强网络关注重要特征的能力，我们将挤压激励（SE）通道注意力机制嵌入到ResNet34网络中，创建了一个SE-ResNet模型。对该模型进行训练以有效提取和分类特征。使用特定数据集对所开发的方法进行了验证，准确率达到了约98.1%。结果表明所开发的方法在强噪声条件下诊断行星齿轮箱故障方面的有效性和可靠性。
