TITLE:
Fatigue-Consistent Load Extrapolation Based on Tail-Weighted Histogram-Regularized LSTM
AUTHORS:
Yu Bai, Fei Meng
KEYWORDS:
LSTM Sequence Modeling, Load Extrapolation, Rainflow Counting
JOURNAL NAME:
World Journal of Engineering and Technology,
Vol.14 No.1,
January
28,
2026
ABSTRACT: 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.