Natural-language-processing and safety-engineering-based fault identification technique for electrochemical ESSs.

Electrochemical energy storage systems (ESSs) are crucial for grid stability and renewable energy integration, yet their increasing scale and complexity exacerbate safety risks such as thermal runaway and fire. To address these challenges, we propose an integrated fault diagnosis framework that combines natural language processing, deep learning, and safety engineering. A global ESS fault log was compiled and augmented with synthetic cases generated by large language models, ensuring both diversity and balanced representation. Fault information extracted from unstructured reports was analyzed via Bow-Tie and failure mode analyses to identify evolution pathways and key risk factors. For classification, we introduce a self-attention augmented convolutional neural network with a dynamic learning rate, which effectively captures subtle features and long-range dependencies. Our model achieves an accuracy of 94.93% and a macro F1-score of 0.9427, outperforming conventional benchmarks. Beyond classification, the framework links each identified fault to a complete process solution, including preventive measures, emergency responses, and consequence analysis, thereby reducing downtime and enhancing system resilience. In addition, keyword networks and hierarchical clustering reveal hidden associations among fault categories, providing actionable insights for targeted preventive strategies. This work establishes a robust and practical pathway for real-time monitoring, intelligent diagnosis, and proactive risk management in ESSs.