Rolling Bearing Life Prediction Technology Based on Feature Screening and LSTM Model

As one of the important components of industrial equipment, the health condition of rolling bearings will directly affect the operational effectiveness of the equipment. Therefore, to ensure equipment safety and reduce maintenance costs, an intelligent rolling bearing life prediction technology is proposed. Firstly, it extracts the fault information of rolling bearings and introduces Fisher score for feature selection. Simultaneously, a variational modal analysis method on the grounds of improved particle swarm optimization is introduced to achieve denoising of rolling bearing signals. Finally, an improved bidirectional long short-term model is introduced to construct a prediction model and achieve the life prediction of rolling bearings. In the performance analysis of the denoising model, the optimal modal component K value of the denoising model was obtained through experimental analysis as 3, and the optimal penalty factor number was 1000. In the time-domain signal analysis of the two models, the proposed model possesses a more excellent decomposition effect on the original signal compared to the comparative model, and the signal denoising ability is improved by 26.35%. In the prediction of rolling bearing life, the proposed model can accurately predict the early and late life of rolling bearings. For example, when the collection time is 100, the actual remaining life is 0.712, and the proposed model is 0.721, which is better than other models. In the comparison of average absolute error, the proposed model is 0.035, which outperforms other models. This indicates that the proposed rolling bearing life prediction model has excellent predictive performance. The research provides essential technical references for the maintenance of industrial machinery and equipment, as well as equipment life monitoring.

@article{Zhao2024,
  title     = {Rolling Bearing Life Prediction Technology Based on Feature Screening and LSTM Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {3},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Yujun Zhao},
  doi       = {10.14569/IJACSA.2024.0150378},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150378}
}