Explainable Dynamic Graph Neural Networks for Predictive Maintenance in Vehicle Chassis Systems

Wu You, Junwei SU, Sirui ZHANG, Zhijian Li

Abstract: Predictive maintenance is essential for commercial vehicle fleets to reduce unexpected downtime and emergency repair costs. While standardized fault codes (SPN/FMI, representing Suspect Parameter Numbers and Failure Mode Indicators) assist in diagnosis, their temporal and spatial inconsistency limits the effectiveness of conventional time-series models in identifying high-cost failures. We propose a Hybrid Node-level Relationship-based Graph Convolutional Network with Random Forest (NRP-GCN-RF), which encodes fault interactions as graphs to capture non-temporal dependencies. Built on a real-world dataset from Dongfeng Motor Corporation, our study follows a dual-task design. (1) We construct a predictive model using graph neural networks (GCN) and random forests (RF) to forecast emergency repair costs and fault categories based on chassis-level fault sequences. (2) In parallel, we apply the Apriori algorithm to mine frequent co-occurring SPN-FMI pairs, revealing interpretable fault patterns and subsystem-level dependencies. This interpretable analysis complements the graph-based model by supporting feature design and failure diagnostics. Experiments show that our approach achieves 98.93% accuracy, raises high-cost failure precision from 60% to 95%, and improves recall by 25%, offering a robust and explainable solution for predictive maintenance in commercial fleets.

Submission Number: 201