Ahang Maryam, Jalayer Masoud, Shojaeinasab Ardeshir, Ogunfowora Oluwaseyi, Charter Todd, Najjaran Homayoun
Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada.
Department of Mechanical Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada.
Sensors (Basel). 2022 Jul 20;22(14):5413. doi: 10.3390/s22145413.
Bearings are vital components of rotating machines that are prone to unexpected faults. Therefore, bearing fault diagnosis and condition monitoring are 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 are ample as systems usually work in desired conditions. On the other hand, fault data are rare, and in many conditions, there are 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) was introduced. Trained on the normal and fault data on actual fault conditions, this algorithm generates fault data from normal data of target conditions. The proposed method was validated on a real-world bearing dataset, and fault data were generated for different conditions. Several state-of-the-art classifiers and visualization models were implemented to evaluate the quality of the synthesized data. The results demonstrate the efficacy of the proposed algorithm.
轴承是旋转机械的关键部件，容易出现意外故障。因此，轴承故障诊断和状态监测对于降低众多行业的运营成本和停机时间至关重要。在各种生产条件下，轴承可在一系列负载和速度下运行，这会导致与每种故障类型相关的不同振动模式。正常数据很充足，因为系统通常在理想条件下运行。另一方面，故障数据很少，而且在许多情况下，没有针对故障类别的记录数据。获取故障数据对于开发能够提高操作性能和安全性的数据驱动型故障诊断工具至关重要。为此，引入了一种基于条件生成对抗网络（CGAN）的新算法。该算法在实际故障条件下的正常数据和故障数据上进行训练，从目标条件的正常数据中生成故障数据。所提出的方法在一个真实世界的轴承数据集上得到了验证，并针对不同条件生成了故障数据。实施了几种先进的分类器和可视化模型来评估合成数据的质量。结果证明了所提算法的有效性。
