Dual-Aspect Self-Attention Based on Transformer for Remaining Useful Life Prediction

Remaining useful life (RUL) prediction is one of the key technologies of condition-based maintenance (CBM), which is important to maintain the reliability and safety of industrial equipment. Massive industrial measurement data has effectively improved the performance of the data-driven-based RUL prediction method. While deep learning has achieved great success in RUL prediction, existing methods have difficulties in processing long sequences and extracting information from the sensor and time step aspects. In this article, we propose dual-aspect self-attention based on transformer (DAST), a novel deep RUL prediction method, which is an encoder–decoder structure purely based on self-attention without any…

• SU
  Shandong University

  Award: 62420089964188
• NN
  National Natural Science Foundation of China

  Award: 62102228
• NS
  Natural Science Foundation of Shandong Province

  Award: ZR2021QF063