融合图标签传播和判别特征增强的工业机器人关键部件半监督故障诊断方法

RV reducer is the critical component of industrial robot. Its mechanical faults will reduce the machine performance. The monitoring and intelligent fault diagnosis is of great significance. Traditional fault diagnosis methods assume that sufficient labeled data are available, while labeling the fault data is labor-consuming in practice. To solve this problem, a novel semi-supervised fault diagnosis method via graph label propagation and discriminative feature enhancement is proposed for RV reducer. First, the pseudo labels are produced by label propagation algorithm for unlabeled data. By using entropy, the pseudo labels are associated with a weight reflecting its certainty, so as to reduce the effect of pseudo label noise. Then, by optimizing the metric learning loss in deep embedding space for few labeled samples, the discriminative ability of feature graph is enhanced. The effectiveness of proposed method is demonstrated in the fault dataset of actual industrial robot RV reducer. The results show that the proposed semi-supervised method can produce accuracy pseudo labels, and achieve superior fault identification rate with few labeled samples.