Department of Computer Science, Yonsei University, Seoul 03722, Korea.
Sensors (Basel). 2021 Feb 18;21(4):1417. doi: 10.3390/s21041417.
In recent years, various deep learning models have been developed for the fault diagnosis of rotating machines. However, in practical applications related to fault diagnosis, it is difficult to immediately implement a trained model because the distribution of source data and target domain data have different distributions. Additionally, collecting failure data for various operating conditions is time consuming and expensive. In this paper, we introduce a new transformation method for the latent space between domains using the source domain and normal data of the target domain that can be easily collected. Inspired by semantic transformations in an embedded space in the field of word embedding, discrepancies between the distribution of the source and target domains are minimized by transforming the latent representation space in which fault attributes are preserved. To match the feature area and distribution, spatial attention is applied to learn the latent feature spaces, and the 1D CNN LSTM architecture is implemented to maximize the intra-class classification. The proposed model was validated for two types of rotating machines such as a dataset of rolling bearings as CWRU and a gearbox dataset of heavy machinery. Experimental results show the proposed method has higher cross-domain diagnostic accuracy than others, therefore showing reliable generalization performance in rotating machines operating under various conditions.
近年来，各种深度学习模型已被开发用于旋转机械的故障诊断。然而，在与故障诊断相关的实际应用中，由于源数据和目标域数据的分布不同，很难立即实现训练好的模型。此外，收集各种工作条件下的故障数据既耗时又昂贵。在本文中，我们引入了一种新的方法，通过使用源域和可以轻松收集的目标域正常数据来对域间的潜在空间进行转换。受词嵌入领域中嵌入空间语义转换的启发，通过转换保留故障属性的潜在表示空间，可以最小化源域和目标域之间的分布差异。为了匹配特征区域和分布，应用空间注意力来学习潜在特征空间，并实现 1D CNN LSTM 架构以最大化类内分类。该模型已针对两种类型的旋转机械进行了验证，例如 CWRU 的滚动轴承数据集和重型机械的齿轮箱数据集。实验结果表明，与其他方法相比，所提出的方法具有更高的跨域诊断精度，因此在各种条件下运行的旋转机械中具有可靠的泛化性能。
