FD-SE-LLM: A Semantic-Enhanced Large Language Model Framework for Fault Diagnosis of Hydropower Carbon Brush Bearings

Abstract

Cross-condition fault diagnosis remains a fundamental challenge in intelligent operation and maintenance of hydropower equipment, where deep learning methods suffer 20%−30% accuracy degradation under operating condition drifts. Large language models (LLMs), through massive multi-domain pre-training, offer cross-domain generalization that may help overcome this limitation. However, applying LLMs to industrial vibration signals faces several difficulties: a modality gap between continuous physical time series and discrete semantic sequences, multi-fault class aliasing in the semantic space, and limited sensitivity to transient fault pulses. This paper proposes FD-SE-LLM, a Semantic-Enhanced Large Language Model Framework for Fault Diagnosis targeting hydropower carbon brush bearings. The framework includes Time-domain Semantic Cross-Correlation (TSCC) to project periodic and transient features into the LLM semantic space, a Fault-specific Multi-class Conditional Variational Autoencoder (FM-CVAE) to disentangle fault class-specific representations from environmental noise, and an embedded Bearing Time-Adapter to enhance transient pulse sensitivity. Experiments on hydropower, CWRU, and JNU datasets show that FD-SE-LLM achieves 91.5% average cross-condition accuracy, outperforming deep learning baselines by 6.8−15.1 pp and existing LLM-based approaches by 3.9−6.3 pp.