Transformer-Based Predictive Maintenance for Risk-Aware Instrument Calibration
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
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2026
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| _version_ | 1866915878023462912 |
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| author | Parthasarathy, Adithya Kirubakaran, Aswathnarayan Muthukrishnan Deshpande, Akshay Bodala, Ram Sekhar Malempati, Suhas Chockalingam, Nachiappan Punniyamoorthy, Vinoth Aarella, Seema Gangaiah |
| author_facet | Parthasarathy, Adithya Kirubakaran, Aswathnarayan Muthukrishnan Deshpande, Akshay Bodala, Ram Sekhar Malempati, Suhas Chockalingam, Nachiappan Punniyamoorthy, Vinoth Aarella, Seema Gangaiah |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20297 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Transformer-Based Predictive Maintenance for Risk-Aware Instrument Calibration Parthasarathy, Adithya Kirubakaran, Aswathnarayan Muthukrishnan Deshpande, Akshay Bodala, Ram Sekhar Malempati, Suhas Chockalingam, Nachiappan Punniyamoorthy, Vinoth Aarella, Seema Gangaiah Machine Learning Artificial Intelligence 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. |
| title | Transformer-Based Predictive Maintenance for Risk-Aware Instrument Calibration |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2603.20297 |