Zhang Rui, Zeng Zhiqiang, Li Yanfeng, Liu Jiahao, Wang Zhijian
School of Mechanical Engineering, North University of China, Taiyuan 030051, China.
Entropy (Basel). 2022 Oct 31;24(11):1578. doi: 10.3390/e24111578.
Bearing is a key part of rotating machinery. Accurate prediction of bearing life can avoid serious failures. To address the current problem of low accuracy and poor predictability of bearing life prediction, a bearing life prediction method based on digital twins is proposed. Firstly, the vibration signals of rolling bearings are collected, and the time-domain and frequency-domain features of the actual data set are extracted to construct the feature matrix. Then unsupervised classification and feature selection are carried out by improving the self-organizing feature mapping method. Using sensitive features to construct a twin dataset framework and using the integrated learning CatBoost method to supplement the missing data sets, a complete digital twin dataset is formed. Secondly, important information is extracted through macro and micro attention mechanisms to achieve weight amplification. The life prediction of rolling bearing is realized by using fusion features. Finally, the proposed method is verified by experiments. The experimental results show that this method can predict the bearing life with a limited amount of measured data, which is superior to other prediction methods and can provide a new idea for the health prediction and management of mechanical components.
轴承是旋转机械的关键部件。准确预测轴承寿命可避免严重故障。为解决当前轴承寿命预测精度低、可预测性差的问题，提出了一种基于数字孪生的轴承寿命预测方法。首先，采集滚动轴承的振动信号，提取实际数据集的时域和频域特征以构建特征矩阵。然后通过改进自组织特征映射方法进行无监督分类和特征选择。利用敏感特征构建孪生数据集框架，并使用集成学习CatBoost方法补充缺失数据集，形成完整的数字孪生数据集。其次，通过宏观和微观注意力机制提取重要信息以实现权重放大。利用融合特征实现滚动轴承的寿命预测。最后，通过实验验证了所提方法。实验结果表明，该方法能够利用有限的测量数据预测轴承寿命，优于其他预测方法，可为机械部件的健康预测与管理提供新思路。
