基于不对称的残余收缩编码器承载健康指数构造和剩余寿命预测
Baobao Zhang1, Jianjie Zhang2, Peibo Yu2
1College of Software, Xinjiang University, Urumqi 830091, China.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
这项研究引入了一个不对称的残余收缩卷积自编码器 (ARSCAE),用于预测轴承的剩余使用寿命 (RUL). 该ARSCAE模型有效地从原始振动数据构建健康指标,提高RUL预测准确度.
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:
- 预测轴承的剩余使用寿命 (RUL) 对机械系统可靠性至关重要.
- 目前用于构建健康指标 (HI) 的方法通常依赖于手动特征提取和值,限制其有效性.
- 传统方法可能无法充分利用原始振动数据中的丰富信息.
研究的目的:
- 提出一种新的深度学习模型,用于构建滚动轴承的有效健康指标 (HI).
- 解决传统的HI施工方法在利用原始振动数据方面的局限性.
- 提高轴承的剩余使用寿命 (RUL) 预测的准确性和能力.
主要方法:
- 开发一个不对称的残余收缩卷积自编码器 (ARSCAE) 模型.
- 采用编码器的不对称结构和软值来降低振动信号中的噪声.
- 在解码器中使用卷积和聚合层进行数据重建和HI生成.
- 从原始的振动信号直接构建HI,而不需要手动功能工程.
主要成果:
- 该ARSCAE模型成功地从原始振动信号直接构建健康指标.
- 与现有的模型进行比较,证明了优越的HI建筑能力.
- 在FEMTO数据集上的实验验证证证了ARSCAE生成的HI的增强寿命预测性能.
结论:
- 拟议的ARSCAE模型提供了一种更有效的方法来构建带有RUL预测的健康指标.
- 通过充分利用振动信号信息,ARSCAE克服了传统方法的局限性.
- 该模型显示了提高机械系统可靠性和预测性维护的巨大潜力.
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