Given the limited availability of accurately labeled data in fault diagnosis across various industrial scenarios, we proposed a Gradual Conditional Domain Adversarial Network (GCDAN) incorporating various fault categories and rotating speeds. We constructed a prototype system for collecting three-dimensional vibration data samples and modified the network structure to accommodate the input. Inspired by the generalization capability of cross-device scenarios, we adopted CDAN as the main component. To overcome the performance degradation caused by the source and target domains with substantial distribution differences, we introduced Gradual Domain Adaptation into our algorithm. Unlabeled data samples obtained from the intermediate domains were used to train a sequence of CDANs. Experimental comparison results confirmed the effectiveness of the 3-D input data and its network alteration. Additionally, GCDAN performed better over challenging transfer tasks compared to the existing state-of-art algorithms in terms of prediction accuracy and multi-class classification metrics.

中文翻译：

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使用渐进条件域对抗网络进行轴承故障诊断

鉴于在各种工业场景的故障诊断中准确标记的数据的可用性有限，我们提出了一种包含各种故障类别和转速的渐进条件域对抗网络（GCDAN）。我们构建了一个用于收集三维振动数据样本的原型系统，并修改了网络结构以适应输入。受到跨设备场景泛化能力的启发，我们采用CDAN作为主要组件。为了克服由于源域和目标域具有显着分布差异而导致的性能下降，我们在算法中引入了渐进域适应。从中间域获得的未标记数据样本用于训练 CDAN 序列。实验比较结果证实了 3D 输入数据及其网络更改的有效性。此外，与现有最先进的算法相比，GCDAN 在预测准确性和多类分类指标方面在具有挑战性的传输任务上表现更好。