Progressive Fine-Tuning and Adaptive Dual-Path Evidence Augmentation Framework for Industrial Equipment Maintenance

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Industrial equipment-maintenance knowledge is scattered across technical manuals, historical work orders, and field experience. Rapidly retrieving these heterogeneous sources and generating accurate guidance are essential to reducing downtime losses and ensuring operational safety. General-purpose large language models perform well in open-domain question answering, but their pretraining corpora lack industrial maintenance terminology and diagnostic reasoning patterns, causing severe mistakes in direct applications. Semantic-similarity-based retrieval-augmented generation can introduce domain knowledge to reduce hallucinations, yet it cannot preserve causal direction and procedural-order constraints. This limitation can logically misalign retrieved evidence and misguide maintenance decisions. Field queries also vary in wording and information completeness and often contain equipment abbreviations, colloquial transcriptions, and omitted key details, making fixed retrieval and response strategies unreliable. Our framework combines progressive fine-tuning with dual-path evidence fusion. A perplexity-driven sample-difficulty partition organizes two-stage fine-tuning so that the model learns basic domain knowledge before complex diagnostic reasoning, mitigating domain hallucinations. Parallel knowledge-graph and vector retrieval uses graph topology to preserve recorded relation directions and source procedures to supply operating-sequence information. A three-level router driven by retrieval necessity and entity-matching status selects direct answering, vector retrieval, or joint graph–vector retrieval for queries with different levels of information completeness. Independent tests, noisy-query experiments, and blinded expert evaluation show improved answer accuracy, logical consistency, and noise robustness. Relative to the base model, two-stage fine-tuning improves BLEU and ROUGE by approximately 14% and 7%, respectively. The results indicate that progressive domain learning and structured external evidence can provide reliable decision support for industrial equipment maintenance.