RELATE: Health-Relative Evidence for Structured Industrial Diagnosis with LLMs

Industrial diagnosis spans signals with different sampling rates, fault vocabularies, and diagnostic attributes. We introduce RELATE, a framework that pairs source-rate query features with healthy training references for condition classification and structured record prediction. RELATE-MLP classifies the complete query–reference representation; RELATE-Gen augments the same representation with temporal features and maps the fused evidence to 4 continuous tokens for candidate-conditioned Qwen generation. We evaluate both routes on MOSAIC (Multi-source Operational Signals with Annotated Industrial Conditions), which covers 12 acoustic, vibration, current, and voltage settings under fixed group-level splits. RELATE-MLP reaches 80.60% setting-macro accuracy and 78.36% setting-macro F1 on 3,598 frozen-test groups. RELATE-Gen reaches 78.65/77.01% accuracy/F1 and 68.86% setting-macro Joint EM on records assembled by per-field voting across views. Across three fixed validation runs under the selected autoregressive objective, health-relative evidence improves accuracy by 5.07 points on average; the complementary temporal evidence and learned fusion add 4.56 points.