Artificial Intelligence Based Predictive Maintenance for Electric Buses

2025-10-27

AI Summary

Electric bus powertrain and battery systems exhibit high complexity, rendering conventional time-based maintenance inadequate for real-time detection of multidimensional CAN bus anomalies. Method: This paper proposes a hybrid graph-structured feature selection method integrating statistical filtering and optimized community detection to enhance identification of critical fault parameters and model interpretability. It first screens features using Pearson correlation, Cramér’s V, and ANOVA; constructs a parameter association graph via InfoMap; and employs an ensemble of SVM, Random Forest, and XGBoost. Data imbalance is addressed via SMOTEEN and binary undersampling; LIME augments model explainability. Contribution/Results: Experiments demonstrate significant improvements in alarm prediction accuracy and fault feature localization precision, alongside reduced response latency. The approach validates the feasibility and industrial applicability of AI-driven predictive maintenance in real-world public transit operations.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Predictive maintenance (PdM) is crucial for optimizing efficiency and minimizing downtime of electric buses. While these vehicles provide environmental benefits, they pose challenges for PdM due to complex electric transmission and battery systems. Traditional maintenance, often based on scheduled inspections, struggles to capture anomalies in multi-dimensional real-time CAN Bus data. This study employs a graph-based feature selection method to analyze relationships among CAN Bus parameters of electric buses and investigates the prediction performance of targeted alarms using artificial intelligence techniques. The raw data collected over two years underwent extensive preprocessing to ensure data quality and consistency. A hybrid graph-based feature selection tool was developed by combining statistical filtering (Pearson correlation, Cramer's V, ANOVA F-test) with optimization-based community detection algorithms (InfoMap, Leiden, Louvain, Fast Greedy). Machine learning models, including SVM, Random Forest, and XGBoost, were optimized through grid and random search with data balancing via SMOTEEN and binary search-based down-sampling. Model interpretability was achieved using LIME to identify the features influencing predictions. The results demonstrate that the developed system effectively predicts vehicle alarms, enhances feature interpretability, and supports proactive maintenance strategies aligned with Industry 4.0 principles.

Problem

• Predicting electric bus maintenance needs using AI
• Analyzing complex CAN Bus data with graph methods
• Enhancing maintenance strategies through machine learning

Innovation

• Graph-based feature selection for CAN Bus data analysis
• Hybrid statistical and community detection algorithms for optimization
• Machine learning models with LIME for interpretable predictions