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

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.