Validation Study on Fault Diagnosis of Elevator Traction Drive Systems Based on Public Motor-Drive Data and Multimodal Signal Analysis

Abstract

To address the difficulty of obtaining real fault data from elevator traction drive systems and the potential overestimation caused by random window splitting, this study uses two public motor-drive datasets to examine how validation protocols, signal modalities, and noise conditions affect diagnostic evaluation, rather than to claim direct validation of field performance in actual elevator systems. A PMSM inverter-drive fault diagnosis dataset is used as the main dataset to evaluate multiclass classification performance based on electrical, thermal, and derived features. A multimodal MOTOR dataset is used as an independent secondary dataset to analyze the effects of validation protocols, signal modalities, and noise disturbance on model performance. The results show that Random Forest achieves a Macro-F1 of 0.9901 under random splitting on the PMSM dataset. On the MOTOR dataset, the Macro-F1 reaches 0.9682 under random splitting but decreases to 0.5856 under strict block-split validation, indicating that random window splitting may substantially overestimate generalization performance for continuous signal data. The modality ablation results show that the vibration-only modality performs best under strict block-split validation, with a Macro-F1 of 0.6420, whereas the noise analysis indicates that this modality is sensitive to disturbance. The results show that public motor-drive data can provide a reproducible methodological test bed for studying evaluation bias and signal reliability, but they should not be interpreted as direct evidence of diagnostic performance in actual elevator systems.