Quanling Zhang

Bearings are widely utilized in industrial applications. Their condition critically influences the stability, reliability, and accuracy of the equipment. Traditional fault diagnosis approaches typically depend on comprehensive fault data for accurate identification. However, challenges such as inadequate fault data and elevated equipment damage costs often limit the effectiveness of data-driven fault diagnosis methods in many industrial contexts. The digital twin information model can generate a virtual bearing entity, enabling the simulation of its operation and the generation of fault data under varying conditions. In this paper, a digital twin information model and measured data fusion based bearing fault diagnosis method is proposed. Initially, a virtual twin information model of the bearing is constructed using the finite element method. This model allows for the injection of fault depth and the generation of corresponding fault feature data. Subsequently, an optimization process is conducted to enhance domain adversarial networks and improve data feature mapping between real and virtual domains. Finally, a transformer feature extractor is utilized for effective fusion of multi-source features, facilitating fault diagnosis modeling. The case study results demonstrate that the enhanced features derived from fused data significantly improves diagnostic performance. The proposed method represents a substantial advancement in bearing health status evaluation and fault prediction.

This paper investigates an observer-based secure control method for networked non-Lipschitz nonlinear systems subject to unknown nonlinearities, external disturbances, sensor noises, and intermittent denial-of-service (DoS) attacks. Multi-layer neural networks (MNNs) are adopted to compensate for non-smooth, non-Lipschitz terms, guaranteeing bounded approximation errors. A resilient high-gain observer fused with the MNN is developed to continuously reconstruct system states. When DoS attacks block sensor channels, the observer acts as a virtual dynamic engine to substitute for lost real-time measurements, providing uninterrupted feedback to the controller. Furthermore, to optimize communication efficiency, an observer-based static event-triggered mechanism (SETM) coupled with a hold-input strategy is integrated. Employing the Lyapunov–Krasovskii functional method, sufficient conditions are derived to prove that the closed-loop system remains uniformly ultimately bounded (UUB) under the joint effects of approximation errors, disturbances, and attacks. Simulation results on a two-link manipulator demonstrate that the proposed secure control scheme effectively counters aggressive DoS attacks while achieving a 56.8% reduction in network transmissions compared with conventional periodic sampling paradigms, striking a favorable balance between tracking accuracy and resource efficiency.

Regenerative medicine is increasingly leveraging the synergies between bioinspired conductive biomaterials and 3D/4D bioprinting to replicate the native electroactive and hierarchical microenvironments essential for functional tissue restoration. However, a critical gap remains in the intelligent integration of these technologies to achieve dynamic, responsive tissue regeneration. This review introduces a “bioinspired material–printing–function” triad framework to systematically synthesize recent advances in: (1) tunable conductive materials (polymers, carbon-based systems, metals, MXenes) designed to mimic the electrophysiological properties of native tissues; (2) advanced 3D/4D printing technologies (vat photopolymerization, extrusion, inkjet, and emerging modalities) enabling the fabrication of biomimetic architectures; and (3) functional applications in neural, cardiac, and musculoskeletal tissue engineering. We highlight how bioinspired conductive scaffolds enhance electrophysiological behaviors—emulating natural processes such as promoting axon regeneration cardiomyocyte synchronization, and osteogenic mineralization. Crucially, we identify multi-material 4D bioprinting as a transformative bioinspired approach to overcome conductivity–degradation trade-offs and enable shape-adaptive, smart scaffolds that dynamically respond to physiological cues, mirroring the adaptive nature of living tissues. This work provides the first roadmap toward intelligent electroactive regeneration, shifting the paradigm from static implants to dynamic, biomimetic bioelectronic microenvironments. Future translation will require leveraging AI-driven bioinspired design and organ-on-a-chip validation to address challenges in vascularization, biosafety, and clinical scalability.

With the large-scale integration of renewable energy into distribution networks, the intermittency and uncertainty of renewable generation pose significant challenges to the voltage security of the power grid under extreme scenarios. To address this issue, this paper proposes an optimal scheduling method for power grids under extreme scenarios, based on an improved Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. By simulating potential extreme scenarios in the power system and formulating targeted secure scheduling strategies, the proposed method effectively reduces trial-and-error costs. First, the time series clustering method is used to construct the extreme scene dataset based on the principle of maximizing scene differences. Then, a mathematical model of power grid optimal dispatching is constructed with the objective of ensuring voltage security, with explicit constraints and environmental settings. Then, an interactive scheduling model of distribution network resources is designed based on a multi-agent algorithm, including the construction of an agent state space, an action space, and a reward function. Then, an improved MADDPG multi-agent algorithm based on specific information fusion is proposed, and a hybrid optimization experience sampling strategy is developed to enhance the training efficiency and stability of the model. Finally, the effectiveness of the proposed method is verified by the case studies of the distribution network system.

Fault diagnosis of rotating machinery is widely recognized as a challenging problem. Recent advances in combining convolutional neural networks (CNNs) and Transformers have expanded the capabilities of intelligent fault diagnosis. However, in real-world industrial settings, three critical challenges substantially affect fault diagnosis performance: resource-constrained deployment environments, variable operating conditions, and cross-domain adaptability requirements. These challenges frequently undermine the efficacy of existing algorithms, particularly in sustaining robust performance while meeting lightweight implementation requirements. To address these challenges, a novel lightweight fault diagnosis framework, termed ARE-MGCAFormer, is introduced in this paper. Firstly, an Adaptive Residual Enhancement (ARE) block is specifically designed to extract multi-local receptive field features from vibration signals. The integrated gating mechanism utilizes learnable parameters to adaptively balance the contributions of deep separable convolutions and inverse residual blocks, dynamically adjusting to varying signal features and noise levels while maintaining a lightweight design. Secondly, a Multi-Group Coordinate Attention (MGCA) mechanism is incorporated to effectively extract critical detailed features across the entire signal range while reducing computational complexity by distributing attention across multiple feature groups. Experimental verification was conducted using the gearbox dataset from Xi’an Jiaotong University (XJTU) and the rolling bearing fault dataset from the University of Ottawa (OU). The results demonstrate that the proposed framework exhibits superior lightweight characteristics and robustness in fault diagnosis tasks compared to recent mainstream frameworks based on CNNs and Transformers.

Hydrocracking represents a complex and time-consuming chemical process that converts heavy oil fractions into various valuable products with low boiling points. It plays a pivotal role in enhancing the quality of products within the oil refining process. Consequently, the development of efficient surrogate models for simulating the hydrocracking process and identifying appropriate solutions for multi-objective oil refining is now an important area of research. In this study, a novel transferable preference learning-driven evolutionary algorithm is proposed to facilitate multi-objective decision analysis in the oil refining process. Specifically, our approach involves considering user preferences to divide the objective space into a region of interest (ROI) and other subspaces. We then utilize Kriging models to approximate the sub-problems within the ROI. In order to enhance the robustness and generalization capability of the Kriging models during the evolutionary process, we transfer the mutual information between the sub-problems in the ROI. To validate the effectiveness as well as efficiency of our proposed method, we undertake a series of experiments on both benchmarks and the oil refining process. The experimental results conclusively demonstrate the superiority of our approach.