基于自适应流形嵌入动态分布对齐的轴承故障诊断

智能故障诊断技术能有效保障机械设备安全运行,传统的轴承故障诊断通常假设标记的源域和未标记的目标域数据服从同一分布.然而,在实际的诊断场景中,轴承数据的条件分布和边缘分布往往不满足同分布假设.此外,在原始欧氏空间执行自适应分布对齐时,特征扭曲难以消除,从而影响故障诊断性能.通过提出一种具有流形特征学习和动态分布对齐的自适...

Bearing fault diagnosis based on adaptive manifold embedded dynamic distribution alignment

Intelligent fault diagnosis technology can effectively guarantee the safe operation of mechanical equipment. Traditional bearing fault diagnosis generally assumes that the labeled source and unlabeled target domain data follow the same distribution. However, the conditional and marginal distributions of bearing data usually do not satisfy the same distribution assumption in actual diagnosis scenarios. Moreover, feature distortions are difficult to eliminate when performing adaptive distribution alignment in the original Euclidean space, which affects the fault diagnosis performance. In this paper, an adaptive bearing fault diagnosis model based on manifold feature learning and dynamic distribution alignment is proposed to address these challenges. First, we construct a geodesic flow kernel in the Grassmann manifold and extract the inherent manifold feature representation associated with the bearing fault information, avoiding data feature distortions. Second, a cross domain adaptive factor is defined by distance to dynamically evaluate the conditional and marginal distributions of manifold features. Finally, a cross domain classifier is solved iteratively to predict the target domain samples under the principle of structural risk minimization. The experimental analysis of multiple indicators shows that the model can effectively avoid feature distortions and use dynamic weights to adjust the relative importance of conditional and marginal distributions between cross domain data, which verifies the effectiveness of the proposed method.