Predictive Modeling of Bearing Degradation: LSTM Neural Networks for Uncertainty Quantification

These MATLAB codes are part of a research project focused on predicting bearing degradation through vibration measurements. The codes implement LSTM (Long Short-Term Memory) neural network models trained under different objectives, including uncertainty quantification and RMSE (Root Mean Square Error) minimization. The objective of the research is to compare the performance of these models in predicting bearing health and assessing the associated uncertainty. Note: The current codes are under embargo access as the corresponding paper has been submitted to the ESCA 11 conference. The codes will be made openly accessible upon acceptance of the paper and during the presentation dates. Please cite our paper when using these codes.

本套MATLAB代码隶属于一项基于振动测量值预测轴承退化的研究项目。本代码实现了在不同训练目标下的长短期记忆网络（Long Short-Term Memory, LSTM）模型，训练目标涵盖不确定性量化与均方根误差（Root Mean Square Error, RMSE）最小化。本研究的目标为对比这些模型在预测轴承健康状态以及评估相关不确定性方面的性能表现。注意：由于相关论文已提交至ESCA 11会议，当前代码暂不对外开放。待论文被录用且在会议展示期间，本代码将正式公开。使用本代码时，请引用我们的相关论文。