滚动轴承的剩余使用寿命预测基于使用振动信号的TCN变压器网络
Xiaochao Jin1, Yaping Ji2, Shiteng Li2
1Xi'an Key Laboratory of Extreme Environment and Protection Technology, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel, Switzerland)
|July 28, 2025
概括
这项研究引入了一种新的健康指数和TCN-变压器模型,用于准确预测滚动轴承的剩余使用寿命 (RUL). 该方法增强了振动信号分析,以改善工业预报中的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:
- 预测剩余的使用寿命 (RUL) 对工业预测和健康管理 (PHM) 至关重要.
- 准确的RUL预测需要先进的数据驱动模型,能够处理来自多传感器数据的长时间序列.
- 深度学习模型对于分析轴承操作中的振动趋势,用于故障诊断和RUL估计至关重要.
研究的目的:
- 开发一种新的方法,从振动信号中构建健康指数 (HI),以描述滚动轴承的性能.
- 提出一个新的基于TCN转换器的RUL预测模型,有效地整合振动信号的本地和全球特征.
- 为了应对在RUL估计中长时间序列预测的挑战.
主要方法:
- 特性提取,敏感特征选择,维度减小和规范化用于HI构造.
- 开发了一个TCN-变压器模型,集成局部特征的时间卷积网络 (TCN) 和全球特征的变压器.
- 一个功能融合注意模块被设计为无结合TCN和变压器输出.
- 建议的HI和RUL预测方法使用IEEE PHM 2012数据挑战数据集进行了验证.
主要成果:
- 与原来的复合指数相比,拟议的HI施工方法提高了8.69%的平均综合轴承性能指数.
- TCN-变压器模型显示出卓越的RUL预测准确性,将根平均平方误差 (RMSE) 降低了14.62%,平均绝对误差 (MAE) 降低了9.26%.
- 该模型在RUL预测准确度的整体SCORE中实现了13.04%的改进.
结论:
- 开发的健康指数构造方法有效地描述了滚动轴承的性能特征.
- 通过整合本地和全球振动信号特征,TCN-变压器模型为长时间序列RUL预测提供了卓越的方法.
- 提出的方法显示出在各种操作条件下旋转机械的RUL预测的显著有效性和优越性.
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