The State Estimation (SE) method is troubled by heavy computational tasks and poor estimation tracking capability for the large-scale active distribution network. Given the aforementioned difficulty, this paper proposed a novel multi-area Forecasting Aided State Estimation (FASE) strategy to perceive the state of the system effectively. The proposed strategy begins with the implementation of an improved multi-area FASE model. The processing of multi-source measurement data, such as Micro Phasor Measurement Units (μPMUs) and Supervisory Control and Data Acquisition (SCADA), and equivalent load based information interaction reliably complete the FASE of multi areas. Especially, a 3rd degree dimensionality reduction SR-CKF algorithm is designed for local FASE model considering the influence of large-scale distribution networks data on the numerical stability of the estimator. The case study shows the advantages of the proposed strategy in estimation accuracy, efficiency, and numerical stability compared with the existing ones.

Modeling and inferring the intricate stochastic variations of the manufacturing quality still remain a significant challenge, especially when dealing with non-Euclidean functional data, which emerge ubiquitous in manufacturing processes. To address this issue, this study proposes the deep wavelet neural process (DWNP), an innovative deep learning model leveraging the exceptional potential of neural processes (NPs) for modeling high-dimensional stochastic variations and the superiority of geometric deep learning in analyzing non-Euclidean functional data, to facilitate typical manufacturing quality inference tasks characterized by non-Euclidean functional data. Three major works have been done in this study. First, the Laplace–Beltrami operator was manipulated and a fast spectral graph wavelet transform was performed to derive a tailored graph wavelet neural network (GWNN), which possesses the ability to capture and decouple complex variation patterns in typical non-Euclidean functional data in manufacturing. Second, based on the theoretical and structural prototype of NPs, the DWNP was derived and built up, exploiting the GWNN to model the stochastic variations of non-Euclidean functional data. Finally, an experiment was conducted on a real automotive manufacturing process to verify the effectiveness and superiority of the proposed DWNP model, in which two typical non-Euclidean functional data types were investigated.

Graph convolution network can extract structural multidefect information well, and has been widely concerned in the field of structural damage detection. However, it is difficult to locate and evaluate defects of different sizes only using a single graph structure, and the similarity of nodes is destroyed in the aggregation process. Therefore, this article proposes a dual-graph collaborative bidirectional fusion graph convolution network. First, a dual-graph structure including fixed graph and feature graph is constructed to make up for the lack of information caused by a single structure. Then, the multiscale adaptive fusion of the dual-graph structure is carried out in the horizontal direction, and the node similarity is effectively maintained while using the graph structure. The related information of dual-graph structure and time is deeply mined in the vertical direction, and the multilevel and multitemporal spatio-temporal information is flexibly aggregated. Finally, the accurate location and evaluation of the multiple damage of the structure are realized. Two typical structural multidamage case studies are conducted to verify the performance of the proposed model.

This article develops a proactive and reactive service composition (PRSC) method for CMfg based on digital thread. First, the digital thread-driven information interaction architecture is established, based on which the detailed process of PRSC and the constitution of digital thread are designed. Then, the strategy of proactive SC and the process of reactive service adjustment are proposed: the former selectively allocate alternative services for subtasks to balance the quality of service (QoS) and robustness of the composite manufacturing service, the latter implements the decision-making for service exception handling based on digital thread. Finally, a group of simulation experiments and a practical case study are conducted. The results show that the proposed digital thread-driven PRSC method integrates the advantages of proactive planning and reactive adjustment, effectively reduces the service reservation cost of robust service composition and improves the actual QoSs under uncertainties based on the support of near real-time information interaction.