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 inter-sample 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. ASSGC 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 semi-supervised
deep graph construction approaches. It consistently outperforms these baselines across representative graph neural networks 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.