Abstract Despite the recent advances on intelligent data-driven machinery fault diagnostics, large amounts of high-quality supervised data are mostly required for model training. However, it is usually difficult and expensive to collect sufficient labeled data in real industries, and the difficulty in data preparation significantly hinders the application of the intelligent diagnostic methods. In order to address the data sparsity issue with insufficient labeled data, a deep learning-based fault diagnosis method is proposed in this study, exploring additional unsupervised data which are generally easy for collection. A three-stage training scheme is adopted, i.e. pre-training, representation clustering and enhanced supervised learning. The auto-encoder structure is used for feature extraction, and distance metric learning and k-means clustering method are integrated in the neural network architecture for unsupervised learning. Two rotating machinery datasets are used for validations. The proposed method not only achieves promising diagnostic performance on the semi-supervised learning tasks with few labeled data, but also is well suited for pure unsupervised learning problems. The experimental results suggest the proposed method offers a promising approach on exploiting unsupervised data for fault diagnostics.

中文翻译：

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基于深度表征聚类的无监督数据故障诊断方法应用于旋转机械

摘要 尽管智能数据驱动的机械故障诊断最近取得了进展，但模型训练大多需要大量高质量的监督数据。然而，在现实行业中收集足够的标记数据通常困难且昂贵，数据准备的困难极大地阻碍了智能诊断方法的应用。为了解决标记数据不足的数据稀疏问题，本研究提出了一种基于深度学习的故障诊断方法，探索通常易于收集的额外无监督数据。采用三阶段训练方案，即预训练、表征聚类和增强监督学习。自动编码器结构用于特征提取，并将距离度量学习和 k-means 聚类方法集成到神经网络架构中，用于无监督学习。两个旋转机械数据集用于验证。所提出的方法不仅在标记数据很少的半监督学习任务上取得了有希望的诊断性能，而且非常适用于纯无监督学习问题。实验结果表明，所提出的方法为利用无监督数据进行故障诊断提供了一种有前景的方法。但也非常适合纯无监督学习问题。实验结果表明，所提出的方法为利用无监督数据进行故障诊断提供了一种有前景的方法。但也非常适合纯无监督学习问题。实验结果表明，所提出的方法为利用无监督数据进行故障诊断提供了一种有前景的方法。