LLM-Based Intelligent Data Extraction System for Industrial Equipment

Data extraction and processing constitute the cornerstone of quantitative management and operational analysis in industrial process plants. However, most manufacturing facilities currently lack efficient data extraction systems, relying instead on engineers to manually write and execute database queries, which is time-consuming, error-prone, and inflexible when handling diverse data formats or large-scale equipment networks. To address these limitations, this work presents a novel Large Language Model (LLM)-based intelligent framework designed for data extraction and basic data of industrial equipment. The system integrates natural language understanding capabilities with process database schemas, enabling users to perform complex data queries and analyses through natural language prompts. Specifically, it can perform data mining, time-dependent analyses, equipment comparisons, and cross-period performance evaluations when process information is provided. By integrating the process context, the system can further interpret the analysis results to suggest potential operational conditions or abnormalities occurring in the system. Developed in collaboration with a process industry partner, the proposed system was evaluated using six months of real-world operational data from 10 heat exchangers. Experimental results demonstrate that the framework achieves an extraction accuracy exceeding 99% in the online mode and ranging from 92% to 96% in the privacy-preserving offline mode, with a reduced retrieval latency of approximately 2.64 seconds for local inference. Unlike generic chatbots, this framework targets process-specific reasoning, acting as a cognitive assistant that empowers users to interact intelligently with industrial systems. The findings confirm that LLM-assisted extraction effectively addresses the rigidity and inefficiency of conventional approaches while maintaining high reliability and scalability for industrial analytics.