arXiv Machine Learning October 3, 2026 By Tugrul Cabir Hakyemez, Ener Uras Gokhan

Calibrating Prediction Timeliness Through Multi-Objective Hyperparameter Optimization for Remaining Useful Life Prediction

Read the original on arXiv Machine Learning →

The paper investigates how treating the optimization objective as a design variable can improve Remaining Useful Life (RUL) prediction in predictive maintenance. Five model architectures are compared under single‑objective and multi‑objective hyperparameter optimization, with the latter using NSGA‑II and Entropy‑CRITIC weighting to balance accuracy and prediction timeliness. Results on NASA C‑MAPSS and BackBlaze datasets show that multi‑objective optimization reduces directional imbalance in predictions and can alter model rankings, highlighting the importance of objective choice in RUL modeling.

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