对带有时间卷积自动编码器的轴承进行降解评估和剩余使用寿命预测的联合优化
Yifei Ding1, Minping Jia2, Xiaoli Zhao3
1School of Mechanical Engineering, Southeast University, Nanjing 211189, PR China; Centre for Maintenance Optimization and Reliability Engineering, University of Toronto, Toronto M5S 3G8, Canada.
ISA transactions
|February 6, 2024
概括
这项研究引入了一种用于承载预测的新型深度学习架构. 该方法准确地预测剩余使用寿命 (RUL),并使用原始振动信号评估轴承退化.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
背景情况:
- 预测和健康管理 (PHM) 对于轴承的预测性维护至关重要.
- 深度学习已经推进了数据驱动的PHM,但模拟振动信号中的时间依赖性仍然具有挑战性.
研究的目的:
- 为轴承退化评估和剩余使用寿命 (RUL) 预测开发一个联合优化架构.
- 为PHM解决模拟原始振动信号中的时间依赖性的挑战.
主要方法:
- 提出了一个基于时间卷积自动编码器 (TCAE) 的联合优化架构.
- 该架构使用序列对序列模型进行特征提取和时间分布表征 (TDC),并使用非线性回归器进行RUL预测.
- 该框架将降解评估和RUL预测整合到一个端到端的方式,使用原始信号.
主要成果:
- 在多个数据集上实现了高RUL预测准确度,RMSE为0.0832.
- 与最先进的方法相比,拟议的框架显示出更高的性能.
- 通过使用原始振动信号成功集成了降解评估和RUL预测.
结论:
- 拟议的联合优化架构为轴承的PHM提供了显著的进步.
- 该方法处理原始信号并提供准确的RUL预测的能力突出了其实际潜力.
- 这种方法为旋转机械更有效的预测性维护策略铺平了道路.
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