Rolling element bearings faults are one of the main causes of breakdown of rotating machines. Aside from this, due to variation of operating condition, domain shift phenomenon results in important detection performance deterioration. Therefore, cross-domain intelligent fault detection and diagnosis of bearings is very critical for the reliable operation. In this paper, a new intelligent fault diagnosis approach based on tensor-aligned invariant subspace learning and two-dimensional convolutional neural networks (TAISL–2DCNN) is proposed for cross-domain intelligent fault diagnosis of bearings. The vibration signals of bearings fault are first formulated as a third-order tensor via trial, condition and channel. For adapting the source domain and the target domain tensor representations directly, without vectorization, the domain adaptation (DA) approach named TAISL is first proposed for tensor representation in bearing intelligent fault diagnosis field. Then the 2DCNN is utilized to recognize different faults. The performance of the presented algorithm has been thoroughly evaluated through extensive cross-domain fault diagnosis experiments. The verification results confirm that the developed approach is able to reliably and accurately identify different fault categories and severities of bearings when testing and training data are drawn from different distribution.

滚动轴承故障是旋转机械故障的主要原因之一。除此之外，由于操作条件的变化，域偏移现象导致重要的检测性能下降。因此，轴承的跨域智能故障检测与诊断对于可靠运行至关重要。本文提出了一种基于张量对齐的不变子空间学习和二维卷积神经网络（TAISL-2DCNN）的智能故障诊断新方法，用于轴承的跨域智能故障诊断。首先通过试验，条件和通道将轴承故障的振动信号表述为三阶张量。为了直接调整源域和目标域张量表示而无需向量化，首先提出了一种名为TAISL的域自适应（DA）方法，用于轴承智能故障诊断领域中的张量表示。然后利用2DCNN识别不同的故障。通过广泛的跨域故障诊断实验，已对所提出算法的性能进行了全面评估。验证结果证实，当从不同分布中提取测试和训练数据时，所开发的方法能够可靠，准确地识别轴承的不同故障类别和严重程度。