A large number of sensors are typically installed in industrial plants to collect real-time operational data. These sensors monitor data with time series correlation and spatial correlation over time. In previous studies, GNN has built many successful models to deal with time series data, but most of these models have fixed perspectives and struggle to capture the dynamic correlations in time and space simultaneously. Therefore, this paper constructs a multi-scale dynamic graph neural network (MSDG) for anomaly detection in industrial sensor data. First, a multi-scale sliding window mechanism is proposed to input different scale sensor data into the corresponding network. Then, a dynamic graph neural network is constructed to capture the spatial–temporal dependencies of multivariate sensor data. Finally, the model comprehensively considers the extracted features for sequence reconstruction and utilizes the reconstruction errors for anomaly detection. Experiments have been conducted on three real public datasets, and the results show that the proposed method outperforms the mainstream methods.

With the tendency of decentralization into factories, production scheduling among heterogeneous factories has become a prominent concern in industrial demand response, spurring research on distributed heterogeneous welding shop scheduling problem (DHWSP). Moreover, owing to the inevitable occurrence of uncontrollable system disturbance in practical production environment, the processing time of jobs is uncertain rather than deterministic. Thus, a L-R fuzzy number (LRFN) is introduced to tackle the uncertainty of processing time. Furthermore, in the pursuit of sustainable development, energy efficiency has been a significant emphasis from countries. An effective production scheduling in distributed heterogeneous L-R fuzzy welding shop can optimize both production and energy efficiency, but no related research is reported. Thus, to address this research gap, this paper investigates an energy-efficient distributed heterogeneous L-R fuzzy welding shop scheduling problem (EDHFWSP) with the objectives of minimizing makespan and total energy consumption (TEC). To solve this issue, a self-learning discrete artificial bee colony (SDABC) algorithm is proposed. First, a collaborative initialization is presented to yield excellent initial solutions. Second, a self-learning selection strategy is developed to help solutions select a superior neighborhood structure in employed bee phase. Third, a self-learning variable neighborhood search (SVNS) is designed to adaptively select a neighborhood structure for execution in onlooker bee phase. Fourth, an energysaving strategy is devised to further optimize TEC without affecting makespan. Additionally, to verify the effectiveness of SDABC, extensive experiments are performed to compare SDABC with other 5 optimization algorithms. Experimental results validate that SDABC outperforms its competitors.

Developing energy-efficient routing algorithms is key to reducing energy consumption of Wireless Sensor Networks (WSNs), and the WSNs energy-efficient routing is a combinatorial optimization problem. Many researchers try to optimize it with meta-heuristics. However, most meta-heuristics are inappropriate in designing routing algorithms for WSNs, due to the individual coding and iteration rule for WSNs routing problem. To solve these problems, this paper proposes a Whale Swarm Algorithm with Iterative Counter-based Routing (WSA-ICR) algorithm based on the WSA-IC algorithm. The WSA-ICR algorithm considers the energy consumption of each node and path length in the balanced network and designs a new energy efficiency objective function. Then, the WSA algorithm is improved from five aspects: individual coding, individual initialization, distance calculation between two individuals, individual movement rules and local search. The WSA-ICR algorithm is compared with several energy efficiency optimization routing algorithms. The simulation results show that the WSA-ICR algorithm has excellent performance in balancing the energy consumption of the whole network, prolonging the network life cycle and convergence speed.

Fatigue load modeling is crucial for optimizing and assessing the lifespan of floating wind turbines. This study addresses the complex characteristics of fatigue loads on floating wind turbines under the combined effects of wind and waves. We propose a fatigue load modeling approach based on Vine copula theory and machine learning. Firstly, we establish an optimal joint probability distribution model using Vine copula theory for the four-dimensional random variables (wind speed, wave height, wave period, and wind direction), with model fit assessed using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Root Mean Square Error (RMSE). Secondly, representative wind and wave load conditions are determined using Monte Carlo sampling based on the established joint probability distribution model. Thirdly, fatigue load simulations are performed using the high-fidelity simulator OpenFAST to compute Damage Equivalent Load (DEL) values for critical components (blade root and tower base). Finally, utilizing measured wind and wave data from the Lianyungang Ocean Observatory in the East China Sea, simulation tests are conducted. We apply five commonly used machine learning models (Kriging, MLP, SVR, BNN, and RF) to develop DEL models for blade root and tower base. The results indicate that the RF model exhibits the smallest prediction error, not exceeding 3.9%, and demonstrates high accuracy, particularly in predicting flapwise fatigue loads at the blade root, achieving prediction accuracies of up to 99.97%. These findings underscore the effectiveness of our approach in accurately predicting fatigue loads under real-world conditions, which is essential for enhancing the reliability and efficiency of floating wind turbines.