滚动轴承的剩余使用寿命预测基于ECA-CAE和自动成型器
Jianhua Zhong1,2, Huying Li1,2, Yuquan Chen1
1College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.
Biomimetics (Basel, Switzerland)
|January 22, 2024
概括
一种新的深度学习方法可以在没有完整降解数据的情况下预测滚动轴承的寿命. 它使用通道智能的注意编码器和自动模拟器来准确预测剩余的使用寿命.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 对于轴承预测的传统深度学习需要广泛的降解数据集.
- 预测剩余的使用寿命 (RUL) 对工业维护和安全至关重要.
- 在能够在没有完全降解轴承数据的情况下预测RUL的方法中存在差距.
研究的目的:
- 为滚动轴承RUL预测提出一种新的深度学习方法.
- 为了应对有限的完整轴承退化数据集的挑战.
- 为轴承退化趋势开发一种间接预测方法.
主要方法:
- 使用通道智能注意编码器 (CAE) 来从原始振动数据中提取特征.
- 使用基于Autoformer的时间预测模型进行RUL预测.
- 开发特征选择机制,以确定降解预测的相关特征.
主要成果:
- 拟议的特征提取方法在CAE和多层感知-注意编码器上表现出优异的性能,平均平方误差分别减少了0.0059和0.0402.
- 间接预测方法的准确性高于Informer和Transformer模型,平均平方误差减少0.3352和0.1174.
- 该模型有效地预测轴承降解趋势和剩余的使用寿命.
结论:
- 拟议的深度学习模型为滚动轴承寿命预测提供了有效的解决方案,特别是在有限的降解数据的情况下.
- 这种方法在精度和特征提取能力方面取得了显著的改进.
- 这种方法需要进一步的研究和在工业预测中的实际应用.
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