arXiv:2607.06111v1 Announce Type: cross Abstract: 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 Prediction can then fail before the forecasting backbone becomes the main limitation, because the input window no longer represents the real process.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 ass
The increasing sophistication and ubiquity of AI models in industrial settings, coupled with the inherent unreliability of real-world sensor data, necessitates advanced methods for ensuring data credibility.
Ensuring the credibility of input measurements is critical for the reliable deployment of AI in industrial processes, impacting safety, efficiency, and the trustworthiness of autonomous systems.
The proposed LLM-guided approach suggests a new paradigm for industrial process inference, moving beyond traditional numerical and alarm-based methods to enhance the robustness of AI predictions.
- · Industrial automation companies
- · Manufacturers adopting AI solutions
- · Facilities with extensive sensor networks
- · AI/ML model developers
- · Manufacturers relying on unvalidated sensor data
- · Systems with high fault tolerance requirements but lacking advanced data validat
- · Legacy industrial control systems
Industrial AI systems will achieve higher accuracy and reliability by proactively correcting for flawed input measurements.
Increased trust in AI-driven automation will accelerate adoption across critical infrastructure and complex manufacturing.
The methodology could form a basis for self-correcting industrial AI, reducing human oversight requirements and potentially enabling fully autonomous industrial operations.
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Read at arXiv cs.AI