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April 14, 2026Advances in Mechanical EngineeringOpen Access

Residual useful life prediction of rolling bearings based on improved Informer modeling

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Authors

XBXu BaiXLXiaotong LiXZXiaochen Zhang

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Overview

Hybrid architecture predicts accurate Remaining Useful Life in rolling bearings, highlighting enhanced reliability.

Key Points

  • The research aims to improve predictions of the Remaining Useful Life of rolling bearings using a novel hybrid model.
  • Integrated Convolutional Neural Networks with the Informer model
  • Segmented raw sensor signals using a sliding window approach
  • Utilized stacked convolutional layers for feature extraction
  • Leveraged the Informer module for time-series encoding and dependency modeling
  • Achieved state-of-the-art prediction accuracy for Remaining Useful Life
  • Demonstrated superior stability in long-term predictions
  • Remained effective even with reduced training dataset sizes

Cite This Study

Bai et al. (2026) studied this question.

synapsesocial.com/papers/69ddd959e195c95cdefd6b71https://doi.org/10.1177/16878132261438560
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Data-driven Deep Learning Approach for Remaining Useful Life of Rolling Bearings2024 · 3 citations
  2. 2An intelligent hybrid deep learning model for rolling bearing remaining useful life prediction2024 · 25 citations
  3. 3Rolling Bearing Remaining Useful Life Prediction Based on CNN-VAE-MBiLSTM2024 · 6 citations
  4. 4Prediction of the remaining life of rolling bearings based on parallel bidirectional feature fusion2026
  5. 5Remaining useful life prediction method for rolling bearings based on hybrid dilated convolution transfer2024 · 4 citations