Despite the recent success in data-driven fault diagnosis of rotating machines, there are still remaining challenges in this field. Among the issues to be addressed, is the lack of information about variety of faults the system may encounter in the field. In this paper, we assume a partial knowledge of the system faults and use the corresponding data to train a convolutional neural network. A combination of t-SNE method and clustering techniques is then employed to detect novel faults. Upon detection, the network is augmented using the new data. Finally, a test setup is used to validate this two-stage methodology on a centrifugal pump and experimental results show high accuracy in detecting novel faults.

翻译：尽管数据驱动的旋转机械故障诊断近期取得了成功，该领域仍存在待解决的挑战。其中待解决的问题之一是缺乏关于系统在现场可能遇到的各种故障类型的信息。本文假设对系统故障具有部分先验知识，并利用相应数据训练卷积神经网络。随后采用t-SNE方法与聚类技术相结合来检测新型故障。检测到新型故障后，利用新数据对网络进行扩充。最后通过实验台架对离心泵上这一两阶段方法进行验证，实验结果表明该方法在检测新型故障方面具有高精度。