A-SSGC: Adaptive Graph Construction Capturing Physicochemical Commonalities for Industrial Fault Diagnosis

Accurate identification of subtle faults in industrial manufacturing remains a critical challenge, driving increased adoption of machine learning (ML) techniques. However, classical ML models often overlook complex intersample relationships rooted in shared physicochemical properties, thereby compromising diagnostic accuracy. Addressing this, we propose adaptive synergistic similarity graph construction (A-SSGC), a novel algorithm that adaptively fuses multiple graph construction methods. A-SSGC employs an adaptive sparsification strategy, guided by node degrees, to capture physicochemical commonalities among samples effectively. A-SSGC significantly outperforms traditional ML models, basic graph construction techniques, and both unsupervised and semisupervised deep graph construction approaches. It consistently outperforms these baselines across representative graph neural networks (GNNs) on multiple industrial manufacturing datasets. Visualization of the constructed graphs confirms the ability of A-SSGC to reveal physicochemical commonalities, thereby enhancing interpretability and supporting deeper analytical insights. By effectively capturing these commonalities, A-SSGC improves diagnostic performance. It also shows strong potential as a versatile tool for industrial data analysis, contributing to improved automation and reliability in manufacturing processes. Our code and datasets are available at https://github.com/AnguoCYF/A-SSGC