TL;DR
The findings indicate that the PG-GNN model outperforms traditional fault-detection models, such as Support Vector Machines and conventional GNNs, across key evaluation metrics, and support the fact that the PG-GNN model is highly efficient in detecting abnormal faults and abnormalities.
To address the issues of extreme time-consuming bottlenecks such as special heat treatments in the manufacturing of aerospace precision forgings and scheduling failures caused by sudden rush orders, an intelligent scheduling model based on Graph Neural Networks (GNN) and Deep Reinforcement Learning (DRL) is proposed. T...
Complex industrial processes are characterized by strong coupling, nonlinear interactions, and dynamic causal dependencies between variables, posing significant challenges for accurate fault diagnosis. Conventional data-driven methods often fail to effectively exploit structural prior knowledge, limiting their ability...
As mechanical equipment grows more complex, multi-sensor monitoring and data fusion are essential for accurate fault diagnosis. While graph neural networks (GNNs) are popularly used in this field, their performance evaluations remain largely empirical, lacking a unified theoretical framework to explain and assess the...
Accurate fault prediction and remaining useful life (RUL) estimation are essential for predictive maintenance of industrial equipment. However, existing methods mainly focus on time-series signals and often ignore the heterogeneous characteristics and degradation knowledge in tabular equipment data. This study proposes...
This study concludes that the integration of structural mechanics principles with data-driven AI models—particularly through physics-informed neural networks and edge-deployed lightweight models—represents the most promising direction for next-generation onboard intelligent health monitoring systems.
A dynamic prediction method that integrates assembly deviation mechanisms and machine learning and can reduce the assembly out-of-tolerance rate by about 23%, providing a theoretical tool and technical support for high-precision aircraft assembly.
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