Motor Fault Diagnosis and Predictive Maintenance Based on a Fine-Tuned Qwen2.5-7B Model

The rapid growth of electric vehicle requires more rigid safety and reliability requirements for EV motors. But traditional methods struggle to deal with complex conditions, small samples, and cross-dataset generalization. Thus, we propose a framework using a fine-tuned Qwen2.5-7B model for EV motor fault diagnosis and predictive maintenance. It merges current and vibration signals, extracting features via time–frequency analysis to assess the health state of motor. Lightweight fine-tuning with LoRA and QLoRA reduces training costs and improves generalization and real-time capabilities. Experiments show a 96.5% accuracy on single datasets, 91.2% across conditions, and 82.7% with small samples, outperforming CNN and LSTM models. This analysis not only highlights key features like vibration spectral energy and current fluctuation amplitude, but also enhances the interpretability of results. This study introduces a novel approach to EV motor fault diagnosis, demonstrating the great potential of large language models to tackle fault diagnosis of EV motor.