Bearings are one of the vital components of rotating machines that are prone to unexpected faults. Therefore, bearing fault diagnosis and condition monitoring is essential for reducing operational costs and downtime in numerous industries. In various production conditions, bearings can be operated under a range of loads and speeds, which causes different vibration patterns associated with each fault type. Normal data is ample as systems usually work in desired conditions. On the other hand, fault data is rare, and in many conditions, there is no data recorded for the fault classes. Accessing fault data is crucial for developing data-driven fault diagnosis tools that can improve both the performance and safety of operations. To this end, a novel algorithm based on Conditional Generative Adversarial Networks (CGANs) is introduced. Trained on the normal and fault data on any actual fault conditions, this algorithm generates fault data from normal data of target conditions. The proposed method is validated on a real-world bearing dataset, and fault data are generated for different conditions. Several state-of-the-art classifiers and visualization models are implemented to evaluate the quality of the synthesized data. The results demonstrate the efficacy of the proposed algorithm.

翻译：轴承是旋转机械中易发意外故障的关键部件之一。因此，轴承故障诊断与状态监测对于降低许多工业领域的运营成本和停机时间至关重要。在不同生产工况下，轴承会在各种载荷和转速条件下运行，这导致每种故障类型产生不同的振动模式。由于系统通常工作在预期状态，正常数据较为充足。相反，故障数据非常稀缺，且在许多工况下甚至没有记录故障类别的数据。获取故障数据对于开发能够提升运行性能和安全性的数据驱动型故障诊断工具至关重要。为此，本文提出一种基于条件生成对抗网络的新算法。该算法通过实际故障工况下的正常数据和故障数据进行训练，能够从目标工况的正常数据生成故障数据。基于真实世界轴承数据集对所提方法进行验证，并生成了不同工况下的故障数据。采用多种先进分类器和可视化模型评估合成数据的质量，结果证明了该算法的有效性。