A Hybrid LSTM–Weibull Framework for Estimating Phenolic Joint Seal Replacement Needs in the Generating Units of the Santo Antônio Hydropower Plant

Long maintenance outages require advance planning. This article presents a sequential hybrid framework for the paired phenolic joint seals of 50 generating units at the Santo Antônio Hydropower Plant, Brazil. A unit-specific long short-term memory (LSTM) model estimates the time to recorded replacement and supplies the horizon for Monte Carlo propagation using length-indexed Weibull wear models. An empirical hazard yields seal-specific outputs. Their maximum provides a signaling index based on the logical OR rule, under which either seal can trigger a signal, rather than an exact calibrated joint probability. The main historical configuration uses seven inputs, including gross head, on a 10-day grid. The corresponding archive contains 8858 forecasts across 89 of 100 selected cycles, with a pooled timing mean absolute error (MAE) of 33.58 days and disjoint network-training and validation records. Replacing arithmetic averaging with the maximum raises the mean index from 90.526% to 94.230%. A known-horizon check and a latent-wear sensitivity analysis are reported separately. A chronological calendar experiment uses fitted models held fixed during evaluation, separate development data, and a later test period containing 29 replacements. At the 90% cutoff, false-positive rates are 48.57% and 47.43% for descriptive action windows of 100 and 200 days. Precision is 8.84% and 16.89%. Higher cutoffs reduce false positives but can reduce event-warning coverage. A supervised joint-event probability extension improves Brier loss relative to a constant-risk reference while overestimating mean event risk. The results demonstrate a coupling of temporal prediction and stochastic condition projection, but do not establish an optimal cutoff, satisfactory probability calibration, or prospective operational effectiveness.