Trace Before You Conclude: Learning Evidential Reasoning for Industrial Maintenance Report Generation

The signals-to-semantics (S2S) paradigm bridges the modality gap between industrial time series and large language models (LLMs) by translating sensor dynamics into textual descriptions for maintenance report generation. This semantic interface, however, leaves a distinct learning problem: how to trace report claims to supporting observations and how to leverage that evidence to improve root-cause analysis and maintenance recommendations. We introduce TRACE-S2S, a framework that grounds a domain-structured reasoning chain in temporal semantic evidence. The reasoning chain consists of coarse root-cause identification, fine-grained cause analysis, and maintenance action recommendation. Guided by an external maintenance knowledge graph, an Evidence Tracing Policy constructs stage-specific evidence packages that directly condition a frozen reasoner. The resulting reports preserve explicit links from their conclusions to supporting observations and knowledge. Evidence tracing therefore controls how conclusions are formed, rather than merely explaining them afterward. We optimize only the evidence policy through downstream reinforcement learning, leveraging fault labels and sparse expert report annotations to reward accurate root-cause diagnosis and appropriate maintenance actions. Training requires no annotations of evidence links, and both the S2S front-end and report reasoner remain frozen. The policy can additionally inspect previously uninspected signals when evidence is insufficient. We evaluate TRACE-S2S on a multiphase-flow process and a real-world thermal-power process, assessing diagnostic accuracy, maintenance-action quality, report factuality, and evidence traceability. On average, TRACE-S2S achieves the evidence tracing F1 of 71.69%, outperforming the strongest baseline by 31.50%.