Industrial Science Technology Research Center, Pukyong National University, Busan 608737, Korea.
Information Systems, Pukyong National University, Busan 608737, Korea.
Sensors (Basel). 2022 May 30;22(11):4156. doi: 10.3390/s22114156.
Fault diagnosis (FD) plays a vital role in building a smart factory regarding system reliability improvement and cost reduction. Recent deep learning-based methods have been applied for FD and have obtained excellent performance. However, most of them require sufficient historical labeled data to train the model which is difficult and sometimes not available. Moreover, the big size model increases the difficulties for real-time FD. Therefore, this article proposed a domain adaptive and lightweight framework for FD based on a one-dimension convolutional neural network (1D-CNN). Particularly, 1D-CNN is designed with a structure of autoencoder to extract the rich, robust hidden features with less noise from source and target data. The extracted features are processed by correlation alignment (CORAL) to minimize domain shifts. Thus, the proposed method could learn robust and domain-invariance features from raw signals without any historical labeled target domain data for FD. We designed, trained, and tested the proposed method on CRWU bearing data sets. The sufficient comparative analysis confirmed its effectiveness for FD.
故障诊断（FD）在提高系统可靠性和降低成本方面对于建设智能工厂起着至关重要的作用。基于深度学习的方法最近已被应用于 FD，并取得了优异的性能。然而，它们大多数都需要足够的历史标记数据来训练模型，这是困难的，有时甚至是不可用的。此外，大型模型增加了实时 FD 的难度。因此，本文提出了一种基于一维卷积神经网络（1D-CNN）的故障诊断领域自适应和轻量级框架。特别是，1D-CNN 被设计为自动编码器的结构，从源数据和目标数据中提取丰富、稳健的隐藏特征，同时减少噪声。提取的特征通过相关对齐（CORAL）进行处理，以最小化域转移。因此，该方法可以从原始信号中学习到稳健的、与域不变的特征，而无需任何历史标记的目标域数据进行 FD。我们在 CRWU 轴承数据集上设计、训练和测试了所提出的方法。充分的比较分析证实了其在 FD 中的有效性。
