🤖 AI Summary
This study addresses the limitations of traditional fixed-interval calibration, which neglects operational condition–induced variations in sensor drift rates and consequently risks either excessive resource consumption or non-compliance. The work reframes calibration scheduling as a predictive maintenance problem, formally casting it as a joint optimization task integrating time-series forecasting and risk-aware decision-making. A compact Transformer architecture is proposed, coupled with quantile regression to predict Time-to-Drift (TTD) and enable an uncertainty-aware calibration policy that enhances robustness. Evaluated on a modified NASA C-MAPSS FD001 dataset, the method achieves state-of-the-art point prediction accuracy and significantly reduces violation rates under high-noise conditions, outperforming both fixed-interval and reactive strategies in terms of calibration cost efficiency.
📝 Abstract
Accurate calibration is essential for instruments whose measurements must remain traceable, reliable, and compliant over long operating periods. Fixed-interval programs are easy to administer, but they ignore that instruments drift at different rates under different conditions. This paper studies calibration scheduling as a predictive maintenance problem: given recent sensor histories, estimate time-to-drift (TTD) and intervene before a violation occurs. We adapt the NASA C-MAPSS benchmark into a calibration setting by selecting drift-sensitive sensors, defining virtual calibration thresholds, and inserting synthetic reset events that emulate repeated recalibration. We then compare classical regressors, recurrent and convolutional sequence models, and a compact Transformer for TTD prediction. The Transformer provides the strongest point forecasts on the primary FD001 split and remains competitive on the harder FD002--FD004 splits, while a quantile-based uncertainty model supports conservative scheduling when drift behavior is noisier. Under a violation-aware cost model, predictive scheduling lowers cost relative to reactive and fixed policies, and uncertainty-aware triggers sharply reduce violations when point forecasts are less reliable. The results show that condition-based calibration can be framed as a joint forecasting and decision problem, and that combining sequence models with risk-aware policies is a practical route toward smarter calibration planning.