Transformer-Based Predictive Maintenance for Risk-Aware Instrument Calibration

TL;DR

This paper presents a Transformer-based approach to predictive maintenance for instrument calibration, improving scheduling accuracy and reducing violations by forecasting drift times using sensor data.

Contribution

It adapts the NASA C-MAPSS benchmark for calibration, compares various models including Transformers, and demonstrates the effectiveness of uncertainty-aware, risk-based scheduling policies.

Findings

Transformers outperform classical and recurrent models in drift time prediction.

Uncertainty modeling enables conservative scheduling under noisy conditions.

Predictive scheduling reduces calibration costs and violations compared to reactive and fixed policies.

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.