[Submitted on 26 Aug 2026 (v1), last revised 3 Nov 2026 (this version, v2)]
Abstract:Remaining Useful Life (RUL) estimates guide maintenance in semiconductor manufacturing, where a late intervention can disrupt production but an early one discards usable equipment life. A single prediction does not express the uncertainty needed to choose between these outcomes. We combine Deep Learning (DL) sequence models with Simoultaneous Quantile Regression (SQR) to estimate RUL at selectable quantile levels, and evaluate maintenance decisions using Unexpected Breaks (UB), Unexploited Lifetime (UL), and a cost-weighted objective. On the $F_1$ fault mode of the 2018 PHM Data Challenge (PHM18) ion-milling dataset, we compare recurrent, Transformer, and State Space Models (SSM) architectures. Diagonal State Spaced (S4D) gives the strongest overall RUL accuracy across the evaluated quantiles. Varying the quantile and maintenance window exposes the trade-off between failure risk and early intervention; under the examined cost ratios, the resulting decisions have lower cost than the Preventive Maintenance (PvM) baselines.
Submission history
From: Davide Frizzo [view email]
[v1] Wed, 26 Aug 2026 12:40:14 UTC (1,201 KB)
[v2] Tue, 3 Nov 2026 14:36:55 UTC (694 KB)
