This study proposes a Tail-Weighted Histogram-Regularized LSTM (TWHR-LSTM) to extend 10s load signals into longer sequences while preserving fatigue characteristics. The method removes trends, normalizes the signal, and trains a sequence-to-sequence LSTM model using overlapping windows of 2000 data points. The model’s loss function includes reconstruction error, histogram distance, variance regularization, and a penalty for large step changes to focus on extreme rainflow cycles. Compared to traditional parameter and KDE rainflow extrapolation methods, TWHR-LSTM better reproduces the tail of the load range spectrum, with a pseudo-damage ratio error of only 1.2%. It shows superior performance in maintaining fatigue characteristics and signal quality.
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