轴承的剩余使用寿命预测基于卷积注意网络和增强型变压器
Ning Sun1, Jiahui Tang2, Xiaoling Ye2
1School of Automation, Wuxi University, Wuxi, 214105, China.
Heliyon
|October 17, 2024
概括
本研究引入了MDSCT模型,用于使用原始振动数据准确预测滚动轴承的剩余使用寿命 (RUL). 该模型在预测轴承健康方面表现出卓越的性能和稳定性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 滚动轴承对于工业机械至关重要.
- 预测它们的剩余使用寿命 (RUL) 对于维护和防止故障至关重要.
- 现有的方法往往需要广泛的先验知识或复杂的功能工程.
研究的目的:
- 提出一种全新的端到端模型,MDSCT,用于高效和准确的轴承RUL预测.
- 直接利用原始振动信号,最大限度地减少对先前知识的依赖.
- 增强模型捕获复杂信号特征和非线性关系的能力.
主要方法:
- 开发了一个MDSCT模型,其中包括一个MDSC注意力模块,并行深度可分离的卷积和一个高效的注意力机制.
- 集成了一个PPSformer (Patch-embedding和Probesparse自我注意力变压器编码器) 用于本地和全球特征提取.
- 引入了一个改进的自适应激活功能,AdaptH_Swish,用于模拟非线性.
- 使用PHM2012和XJTU-SY数据集通过废除和比较实验验证实模型.
主要成果:
- 该MDSCT模型有效地从退化的轴承信号中提取局部微妙和全球依赖特征.
- 实验结果证实了模型的结构设计效率和合理性.
- 在标准数据集上,MDSCT在三个关键评估指标上显著优于现有方法.
结论:
- MDSCT模型为轴承RUL预测提供了一个强大的和可通用的解决方案.
- 拟议的架构和自适应激活功能提高了预测准确性和效率.
- 这种方法减少了对领域专业知识和在轴承预测中广泛预处理的需求.
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