The speed of rotating parts is often affected by different working conditions in mechanical equipment, which results in more complications in the feature mapping relationship. However, existing methods that address the large fluctuation problem in rotational speed have been formulated merely to improve test accuracy and do not consider the effects of irregular fluctuation frequency on the fault samples located at the class boundary. Thus, to distinguish the health conditions under frequent or irregular fluctuation speeds, this paper explores an enhanced sparse filtering (SF) algorithm based on maximum classifier discrepancy to diagnose the fault conditions caused by speed fluctuation. It considers the superiority of the task-specific decision boundary and adversarial training for the fault diagnosis network. Unlike traditional SF methods, the proposed framework introduces the Wasserstein distance to reduce the domain discrepancy between the source domain and the target domain and then uses the probability output discrepancy of the classifier to locate the fuzzy fault samples on the class boundary. This paper conducts theoretical analysis and experimental comparison and verifies the performance advantages of the framework through bearing and gear experiments under large speed fluctuation conditions. The proposed model also shows an excellent performance even when the speed fluctuates frequently.

机械设备中旋转部件的速度往往受到不同工作条件的影响，导致特征映射关系更加复杂。然而，现有的解决转速大波动问题的方法只是为了提高测试精度而制定的，没有考虑不规则波动频率对位于类边界的故障样本的影响。因此，为了区分频繁或不规则波动速度下的健康状况，本文探索了一种基于最大分类器差异的增强稀疏滤波（SF）算法来诊断速度波动引起的故障状况。它考虑了故障诊断网络的任务特定决策边界和对抗训练的优越性。与传统的SF方法不同，所提出的框架引入了Wasserstein距离来减少源域和目标域之间的域差异，然后利用分类器的概率输出差异来定位类边界上的模糊故障样本。本文通过理论分析和实验对比，通过大转速波动条件下的轴承和齿轮实验验证了该框架的性能优势。即使在速度频繁波动时，所提出的模型也表现出优异的性能。本文通过理论分析和实验对比，通过大转速波动条件下的轴承和齿轮实验验证了该框架的性能优势。即使在速度频繁波动时，所提出的模型也表现出优异的性能。本文通过理论分析和实验对比，通过大转速波动条件下的轴承和齿轮实验验证了该框架的性能优势。即使在速度频繁波动时，所提出的模型也表现出优异的性能。