As failures in rotating machines can have serious implications, the timely detection and diagnosis of faults in these machines is imperative for their smooth and safe operation. Although deep learning offers the advantage of autonomously learning the fault characteristics from the data, the data scarcity from different health states often limits its applicability to only binary classification (healthy or faulty). This work proposes synthetic data augmentation through virtual sensors for the deep learning-based fault diagnosis of a rotating machine with 42 different classes. The original and augmented data were processed in a transfer learning framework and through a deep learning model from scratch. The two-dimensional visualization of the feature space from the original and augmented data showed that the latter’s data clusters are more distinct than the former’s. The proposed data augmentation showed a 6–15% improvement in training accuracy, a 44–49% improvement in validation accuracy, an 86–98% decline in training loss, and a 91–98% decline in validation loss. The improved generalization through data augmentation was verified by a 39–58% improvement in the test accuracy.

中文翻译：

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用于旋转机器故障诊断的综合数据增强和深度学习

由于旋转机器的故障可能会产生严重的影响，因此及时检测和诊断这些机器的故障对于它们的平稳和安全运行至关重要。尽管深度学习提供了从数据中自主学习故障特征的优势，但来自不同健康状态的数据稀缺性通常限制了其仅适用于二元分类（健康或故障）。这项工作提出了通过虚拟传感器进行合成数据增强，以对具有 42 种不同类别的旋转机器进行基于深度学习的故障诊断。原始数据和增强数据在迁移学习框架中进行处理，并从头开始通过深度学习模型进行处理。来自原始数据和增强数据的特征空间的二维可视化表明，后者的数据簇比前者更明显。提议的数据增强显示训练准确度提高了 6-15%，验证准确度提高了 44-49%，训练损失降低了 86-98%，验证损失降低了 91-98%。测试精度提高了 39-58%，验证了通过数据增强改进的泛化。