Work

Uncertainty aware predictive maintenance using a hybrid Transformer with Monte Carlo Dropout and conformal prediction

Year: 2026

Type: article

Abstract: Predictive maintenance (PdM) relies on accurate estimation of the remaining useful life (RUL) to support efficient industrial maintenance. However, most RUL models overlook uncertainty quantification (UQ), which is essential for safety–critical decision-making. This study presents a hybrid uncertainty-aware framework that combines a Transformer backbone with Monte Carlo Dropout (MC Dropout) and Conformal Prediction (CP). The Transformer architecture effectively learns long-range temporal dependencies in sensor data, while MC Dropout approximates epistemic uncertainty arising from model limitations. CP complements this by producing prediction intervals that capture aleatoric variability caused by noise and operating conditions. The framework is validated using NASA’s C-MAPSS FD001 and FD003 datasets. It achieves strong performance on FD001, with MAE 8.11, RMSE 11.71, and a predictive score of 193.6, and on FD003, with MAE 7.21, RMSE 10.50, and R2 0.926. By jointly addressing both uncertainty types, the method yields well-calibrated confidence intervals, enhancing reliability and interpretability in PdM applications.

Source: Ain Shams Engineering Journal

DOI: 10.1016/j.asej.2026.103992

Authors: Chao-Lung Yang, Tamrat Yifter Meles, Atinkut Atinafu Yilma, Melkamu Mengstnew Teshome

Institution: National Taiwan University of Science and Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,200