轴承的剩余使用寿命预测基于优化的VMD和BiLSTM-CBAM
1School of Mechanical and Electrical Engineering, Zhoukou Normal University, Zhoukou, China.
PloS one
|July 18, 2025
概括
本研究引入了一种用于预测滚动轴承剩余使用寿命 (RUL) 的新方法,显著提高了准确性. 该方法使用先进的算法来增强机械安全的RUL预测.
科学领域:
- 机械工程 机械工程
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 对于滚动轴承的现有剩余使用寿命 (RUL) 预测算法,其准确性较低.
- 准确的RUL预测对于确保旋转机械的安全操作至关重要.
研究的目的:
- 为滚动轴承提出一种新且准确的RUL预测方法.
- 提高工业设备预测性维护的可靠性.
主要方法:
- 使用贝卢加优化 (BWO) 算法来优化变量模式分解 (VMD) 参数用于信号分解和重建.
- 提取时间域和频域特征以创建降解特征集.
- 采用改进的卷积块注意模块 (CBAM*),与双向长短期记忆 (BiLSTM) 网络集成,用于RUL预测.
主要成果:
- 拟议的方法实现了较低的平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 与现有模型和对IEEE PHM 2012数据集的废除研究相比.
- 实验结果证明了BWO-VMD-CBAM*-BiLSTM方法在轴承RUL预测方面的优越性和有效性.
结论:
- 这种新方法显著提高了滚动轴承RUL预测的准确性.
- 这项研究为其他类型的设备的RUL预测提供了宝贵的参考资料,提高了机器的安全性和运行效率.
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