Rolling Bearing Reliability Prediction Based on Signal Noise Reduction and RHA-MKRVM

In order to solve the problem of reliability assessment and prediction of rolling bearings, a noise reduction method (CEEMDAN-GRCMSE) based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) combined with generalized refined composite multi-scale sample entropy (GRCMSE) is proposed from the vibration signals to remove the noise from the bearing vibration signals, and then the feature set of the noise-reduced signals is downscaled by using the Uniform manifold approximation and projection(UMAP) algorithm, and the reliability assessment model is established by using a logistic regression algorithm to establish a reliability assessment model, and use the red-tailed hawk algorithm for parameter optimization of the mixed kernel relation vector machine, which is used to predict the bearing state, and finally the predicted state information is brought into the assessment model to obtain the final results. In this paper, the whole life cycle data of rolling bearings from Xi ’an Jiaotong University-Sun Science and Technology Joint Laboratory (XJTU-SY) are used to verify the effectiveness of the proposed method. The superiority of the proposed method is highlighted by comparing the analysis results with those of other AI methods.

@article{Yu2024,
  title     = {Rolling Bearing Reliability Prediction Based on Signal Noise Reduction and RHA-MKRVM},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Yifan Yu},
  doi       = {10.14569/IJACSA.2024.01508116},
  url       = {https://doi.org/10.14569/IJACSA.2024.01508116}
}