LLM-Guided Measurement Credibility Correction for Trustworthy Industrial Process Inference
Industrial prediction and soft sensing depend on credible input measurements. In field deployment, a predictor may receive biased, delayed, stale, or derived measurements that still look plausible. Prediction can then fail before the forecasting backbone becomes the main limitation, because the input window no longer represents the real process. Sensor reconstruction, data reconciliation, and fault-tolerant soft sensing reduce this risk, but they often rely on numerical correlation, alarms, fault labels, or explicit process equations. These assumptions are not always available. A correlated variable can also be an unsafe reference when variables share instruments, derived formulas, soft-s...
| Authors | Youcheng Zong, Runda Jia, Dakuo He |
|---|---|
| Primary category | eess.SY |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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