Rail Crack Propagation Forecasting Using Multi-horizons RNNs
Sara Yasmine Ouerk Olivier Vo Van Mouadh Yagoubi
The prediction of rail crack length propagation plays a crucial role in the
maintenance and safety assessment of materials and structures. Traditional
methods rely on physical models and empirical equations such as Paris law,
which often have limitations in capturing the complex nature of crack growth.
In recent years, machine learning techniques, particularly Recurrent Neural
Networks (RNNs), have emerged as promising methods for time series forecasting.
They allow to model time series data, and to incorporate exogenous variables
into the model. The proposed approach involves collecting real data on the
French rail network that includes historical crack length measurements, along
with relevant exogenous factors that may influence crack growth. First, a
pre-processing phase was performed to prepare a consistent data set for
learning. Then, a suitable Bayesian multi-horizons recurrent architecture was
designed to model the crack propagation phenomenon. Obtained results show that
the Multi-horizons model outperforms state-of-the-art models such as LSTM and
GRU.