MTCAT: A Modern Temporal Convolution and Enhanced Attention Transformer Model for Remaining Useful Life Prediction of Aerospace Self-Lubricating Bearings

As mission-critical components in aircraft flap actuation systems, aerospace self-lubricating bearings play a pivotal role in ensuring operational safety during critical flight phases. Conventional machine learning approaches for remaining useful life (RUL) prediction suffer from limited time-series and nonlinear modelling capabilities. Especially individual models, built on a single algorithmic paradigm, struggle to address the complex, multi-dimensional challenges of RUL prediction. In order to solve the above challenge, this study proposes an innovative hybrid deep learning method integrating modernized temporal convolutional Networks (ModernTCN) with attention-enhanced Transformer (MTCAT) for RUL…

• NN
  National Natural Science Foundation of China

  Awards: LSSIKFJJ202402003, 52275499, 92467101