Energy-Propagation Graph Neural Networks for Enhanced Out-of-Distribution Fault Analysis in Intelligent Construction Machinery Systems

Xinming Li, Meng Li, Jiawei Gu, Yanxue Wang, Jiachi Yao, Jianbo Feng

Abstract: In intelligent fault diagnosis for construction machinery, robust and precise detection of out-of-distribution (OOD) data is crucial for enhancing operational efficiency and reducing downtime. This article introduces the energy-driven graph neural OOD (EGN-OOD) detector, a novel framework designed to address the complexities of OOD data in dynamic Internet of Things (IoT) environments. By integrating graph neural networks with energy-based models, our approach captures intricate fault correlations and improves the accuracy of fault diagnosis. The EGN-OOD framework uses the maximal information coefficient to transform sensor-acquired vibration data, typical in IoT applications, into graph representations. This conversion produces an adjacency matrix that outlines the nonlinear interactions among different fault types. Additionally, the framework includes an energy score-based OOD detection module that redefines classifier logits to create an energy function, enabling precise differentiation between in-distribution (ID) and OOD data. To enhance model robustness in semi-supervised settings, a propagation mechanism-based energy score update scheme is implemented, iteratively refining energy values within the graph. Empirical validation on a framework for monitoring mechanical equipment bearing wear demonstrates the EGN-OOD framework’s exceptional ability to detect and diagnose various fault conditions. This validation confirms the framework’s robust generalization capabilities and precision in fault detection and underscores its integration within IoT infrastructures, facilitating smarter diagnostic processes. The results provide substantial technical support for the intelligent diagnosis of construction machinery, advancing IoT-driven solutions for sustainable and intelligent construction practices.