Lstm-based multi-task method for remaining useful life prediction under corrupted sensor data

HIGHLIGHTS

• who: Kai Zhang and Ruonan Liu from the College of Intelligence and Computing, Tianjin University, Tianjin, China have published the research: LSTM-Based Multi-Task Method for Remaining Useful Life Prediction under Corrupted Sensor Data, in the Journal: Machines 2023, 11, 341. of /2023/
• what: To achieve better RUL prediction performance under this situation the authors propose a novel multi-task method for RUL prediction which is named multi-task deep long short-term memory (MTD-LSTM). The authors utilize the FI to quantitatively evaluate the smoothness of the predicted RUL. The authors aim at . . .
  

   

  

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