Ma Jiancheng, Huang Jinying, Liu Siyuan, Luo Jia, Jing Licheng
School of Computer Science and Technology, North University of China, Taiyuan 030051, China.
School of Mechanical Engineering, North University of China, Taiyuan 030051, China.
Sensors (Basel). 2024 Aug 23;24(17):5475. doi: 10.3390/s24175475.
Rotating machinery is widely used in modern industrial systems, and its health status can directly impact the operation of the entire system. Timely and accurate diagnosis of rotating machinery faults is crucial for ensuring production safety, reducing economic losses, and improving efficiency. Traditional deep learning methods can only extract features from the vertices of the input data, thereby overlooking the information contained in the relationships between vertices. This paper proposes a Legendre graph convolutional network (LGCN) integrated with a self-attention graph pooling method, which is applied to fault diagnosis of rotating machinery. The SA-LGCN model converts vibration signals from Euclidean space into graph signals in non-Euclidean space, employing a fast local spectral filter based on Legendre polynomials and a self-attention graph pooling method, significantly improving the model's stability and computational efficiency. By applying the proposed method to 10 different planetary gearbox fault tasks, we verify that it offers significant advantages in fault diagnosis accuracy and load adaptability under various working conditions.
旋转机械在现代工业系统中广泛应用，其健康状态会直接影响整个系统的运行。对旋转机械故障进行及时、准确的诊断对于确保生产安全、减少经济损失以及提高效率至关重要。传统的深度学习方法只能从输入数据的顶点提取特征，从而忽略了顶点之间关系中所包含的信息。本文提出了一种集成自注意力图池化方法的勒让德图卷积网络（LGCN），并将其应用于旋转机械的故障诊断。SA-LGCN模型将欧几里得空间中的振动信号转换为非欧几里得空间中的图信号，采用基于勒让德多项式的快速局部谱滤波器和自注意力图池化方法，显著提高了模型的稳定性和计算效率。通过将所提出的方法应用于10个不同的行星齿轮箱故障任务，我们验证了该方法在各种工况下的故障诊断精度和负载适应性方面具有显著优势。
