Two-Stage LightGBM Framework for Cost-Sensitive Prediction of Impending Failures of Component X in Scania Trucks

Predictive maintenance (PdM) is vital for ensuring the reliability, safety, and cost efficiency of heavy-duty vehicle fleets. However, real-world sensor data are often highly imbalanced, noisy, and temporally irregular, posing significant challenges to model robustness and deployment. Using multivariate time-series data from Scania trucks, this study proposes a novel PdM framework that integrates efficient feature summarization with cost-sensitive hierarchical classification. First, the proposed last_k_summary method transforms recent operational records into compact statistical and trend-based descriptors while preserving missingness, allowing LightGBM to leverage its inherent split rules without ad-hoc imputation. Then, a two-stage LightGBM framework is developed for fault detection and severity classification: Stage A performs safety-prioritized fault screening (normal vs. fault) with a false-negative-weighted objective, and Stage B refines the detected faults into four severity levels through a cascaded hierarchy of binary classifiers. Under the official cost matrix of the IDA Industrial Challenge, the framework achieves total misclassification costs of 36,113 (validation) and 36,314 (test), outperforming XGBoost and Bi-LSTM by 3.8%–13.5% while maintaining high recall for the safety-critical class (0.83 validation, 0.77 test). These results demonstrate that the proposed approach not only improves predictive accuracy but also provides a practical and deployable PdM solution that reduces maintenance cost, enhances fleet safety, and supports data-driven decision-making in industrial environments.