Tool breakage in manufacturing procedures can damage machined surfaces and machine tools. It is crucial to detect tool breakage in time and promptly respond to it. Due to the safety restrictions imposed in production, failure samples are significantly scarcer than normal samples, and this disequilibrium results in difficulty of failure detection. Therefore, we propose a new imbalanced data learning method for tool breakage detection. The key strategy is to balance the data distribution by producing valuable artificial samples for the minority class using an adversarial generative oversampling model based on a generative adversarial network (GAN). Unlike previous studies using GAN, we use the discriminator to screen samples generated by the generator and achieve effective oversampling. Multiple classifiers are adopted as the decision-making models to perform tool breakage detection. The proposed method is applied to a set of imbalanced experimental tool breakage data collected in a workshop. Compared with the best results of other oversampling solutions, the proposed method improves the breakage detection rate from 93.6% to 100%, which shows its practicability and validity. Additionally, evaluations are performed based on 12 imbalanced benchmark datasets. The results further substantiate the superiority of the proposed method to existing sampling methods.

制造过程中的刀具破损会损坏加工表面和机床。及时检测刀具破损并迅速做出响应至关重要。由于生产中的安全限制，故障样本明显少于正常样本，这种不平衡导致故障检测困难。因此，我们提出了一种新的不平衡数据学习方法用于刀具破损检测。关键策略是通过使用基于生成对抗网络 (GAN) 的对抗生成过采样模型为少数类生成有价值的人工样本来平衡数据分布。与之前使用 GAN 的研究不同，我们使用鉴别器来筛选生成器生成的样本并实现有效的过采样。采用多个分类器作为决策模型进行刀具破损检测。将所提出的方法应用于在车间收集的一组不平衡的实验工具破损数据。与其他过采样方案的最佳结果相比，所提出的方法将破损检测率从93.6%提高到100%，显示了其实用性和有效性。此外，评估是基于 12 个不平衡的基准数据集进行的。结果进一步证实了所提出的方法相对于现有采样方法的优越性。6%～100%，说明其实用性和有效性。此外，评估是基于 12 个不平衡的基准数据集进行的。结果进一步证实了所提出的方法相对于现有采样方法的优越性。6%～100%，说明其实用性和有效性。此外，评估是基于 12 个不平衡的基准数据集进行的。结果进一步证实了所提出的方法相对于现有采样方法的优越性。