Industrial large model-based new energy equipment health state estimation method

To address the limitations of current health state estimation methods for naval vessel energy equipment, such as insufficient cross-device generalization ability and high model adaptation complexity, this study proposes a new energy equipment health state estimation method based on industrial large models. The aim is to quickly develop dedicated estimation models using a unified framework and parameter-efficient fine-tuning strategies, thereby achieving high-precision health state estimation.

An industrial large model is constructed using a pre-trained Transformer architecture, incorporating attention mechanisms and low-rank adaptation (LoRA) techniques. This approach dynamically adjusts key parameters to meet the needs of different tasks. It incorporates a sliding window attention mechanism to capture local dynamic characteristics, a conditional attention mechanism to account for environmental influences, and low-rank adaptation fine-tuning modules to rapidly adapt to new equipment tasks.

Verification experiments were conducted on two typical types of new energy equipment: lithium batteries and fuel cells. The results showed that in the lithium battery dataset, the mean absolute error was no greater than 0.0216, and the minimum determination coefficient was no lower than 0.971 3. In the fuel cell dataset, the mean absolute error was no greater than 0.003 3, and the minimum determination coefficient was no lower than 0.9197. The model demonstrated excellent estimation accuracy and generalization ability.

The method based on industrial large models effectively improves the accuracy and reliability of health state estimation for new energy equipment, offering a novel technical approach for equipment health management. Future work can focus on further enhancing model performance through model optimization, cross-device adaptation, and data processing improvements.