The rotor-bearing system is widely used in various high-end electro-hydraulic equipment, which provides specific support, rotation, and other integral functions. However, the fluctuating working conditions of the rotor-bearing system will cause more significant disordered fluctuations in the measured signals. This article proposes a new algorithm called multiscale deep graph convolutional networks (MS-DGCNs) to alleviate this problem. The designed MS-DGCNs algorithm combines a new multiscale intra-class fine coarse-grained processing and multiscale graph convolution kernels. Accordingly, an intelligent fault diagnosis method based on MS-DGCNs for the rotor-bearing system under fluctuating conditions is designed to learn more feature representations and accuracy. First, a sliding window is employed to divide the collected vibration signals into a series of subsignals. The multiscale signal processing is performed to obtain different degrees of the fine-coarse time series. Then, a graph convolution with the multiscale convolution kernel is designed. Finally, the soft-max classifier is combined for intelligent fault diagnosis. The experimental results of the double-span rotor-bearing system under fluctuating conditions well demonstrate that the method has the higher accuracy and generalization.

Traditional health assessment models work well under the assumption that the test and training samples obey a similar distribution. However, it is practically impossible to eliminate domain shifts between different tasks. Thus, most work tries to establish a data-driven approach via domain adaptation to accomplish transfer learning between different operating conditions. Sufficient target data are needed to participate in the training, which may not normally be available due to most working scenarios being unseen. An adversarial domain generalization framework with regularization learning (ADGR) is proposed for the health assessment to mine latent domains. Also, the latent domain is expanded to the unseen domain as possible. More specifically, the diversity of the sample distribution is augmented by adversarial training and the maximization of the domain discrepancy between the latent and source domains. Meanwhile, self-supervised interdomain regularization and semantical consistent regularization are proposed to mitigate the feature drift of the domain classifier and semantic divergence between source and latent domains. The case study shows that the ADGR-based health assessment approach achieves competitive prediction accuracy under unseen conditions, demonstrating its potential as a diagnostic solution.

The Industrial Internet of Things (IIoT) greatly facilitates prognostics and health management of complex industrial systems, wherein the vast amount of real-time data from the IIoT improves intelligent predictive maintenance of industrial systems. When processing industrial IoT data across devices, traditional subdomain adaptation-based methods ignore the local similarities across domains. Also, if fault classes are used to define subdomains, these methods may not be applicable when the target domain is unlabeled or has limited labels. To address the above challenges, a Graph-embedded Subdomain Adaptation Network (GSAN)-based approach is proposed to predict the remaining useful life under different machines in IIoT. Specifically, a manifold subdomain representation is established by manifold learning and local manifold discrepancies between each pair of manifold subdomains with the highest similarity are minimized. To maintain a divisible margin for each manifold, a self-supervised intra-manifold regularization module is developed. An extensive evaluation of six transfer scenarios is performed, and the experimental results show that GSAN can achieve more significant outcomes. This can provide some guidance for future work on prognostics across devices and subdomains.