基于稀疏注意力机制的齿轮早期磨损故障诊断研究

Research on Gear Early Wear Fault Diagnosis Based on the Sparse Attention Mechanism

在齿轮故障诊断领域中,对齿轮早期磨损故障实现有效诊断具有重要意义。然而,早期磨损故障特征弱,诊断难度大。针对该问题,提出了一种基于稀疏注意力机制的齿轮早期磨损故障诊断模型,采用一种新的稀疏注意力机制结合卷积神经网络,改进传统分段序列注意力机制,实现了具体故障频率定位。应用齿轮箱故障模拟实验数据进行测试验证,相比其他诊断方法,所提方法能够在同等样本条件与计算代价下,实现更为准确全面的诊断,降低分析成本,获得敏感故障特征频率,为齿轮维护提供数据支撑。

In the field of gear fault diagnosis,it is of great significance to effectively diagnose early wear faults of gears.However,early wear fault has weak characteristics and is difficult to diagnose.Thus,a gear early wear fault diagnosis model based on the sparse attention mechanism is proposed to solve this problem.This model uses a new sparse attention mechanism combined with a convolution neural network to improve the traditional sectional attention mechanism and locate the specific fault frequency.The gearbox fault simulation test data is used for test verification.Compared with other diagnosis methods,this new method can achieve a more accurate and comprehensive diagnosis,reduce analysis cost and obtain sensitive fault characteristic frequency under the same sample condition and calculation cost.The conclusion of this method can provide data support for gear maintenance.