Fault diagnosis based on vibration signals in active magnetic bearing-rotor systems is an important research topic. However, it is difficult to obtain discriminative features to represent faults due to the nonlinear and non-stationary characteristics of the vibration signals and diverse sources of failures. Hence, this paper proposes a novel end-to-end learning mechanism of multi-sensor data fusion to learn fault representation based on the structural characteristics of active magnetic bearings. Taking the five displacement sensors of active magnetic bearing as signal sources, generalized shaft orbits are constructed and converted into discrete 2D images. Based these 2D images, a multi-branch convolutional neural network is designed to achieve high discriminative features and fault types. The experiments are performed on the rig supported by active magnetic bearings, and the effectiveness of the proposed algorithm is verified, proving it suitability in cases with changing rotating speeds and sample lengths.

基于振动信号的主动磁悬浮轴承系统故障诊断是一个重要的研究课题。但是，由于振动信号的非线性和非平稳特性以及各种故障源，很难获得表示故障的判别特征。因此，本文提出了一种基于主动磁轴承结构特征的多传感器数据融合端到端学习机制，以学习故障表示。以主动磁轴承的五个位移传感器为信号源，构造了广义轴轨道并将其转换为离散的2D图像。基于这些2D图像，设计了多分支卷积神经网络以实现高判别特征和故障类型。