缓慢的基于特征的特征融合方法对机械的基于相似性的预测
Bin Xue1, Haoyan Xu1, Xing Huang2
1Institute of Process Equipment, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, Zhejiang, China.
ISA transactions
|June 23, 2024
概括
本研究引入了一种用于预测性维护的新功能融合方法,改进了未知故障模式的机器的降解趋势分析. 该方法通过解决多传感器数据差异和正常化操作条件来提高预测准确性.
科学领域:
- 机器学习 机器学习
- 预测性维护是指预测性维护.
- 信号处理 信号处理
背景情况:
- 基于相似性的预测方法对于工业预测性维护至关重要,特别是对于未知故障机制的机器.
- 当前的方法往往无法解释来自多传感器数据的退化趋势的差异,并且在功能融合过程中缺乏操作模式的自动正常化.
研究的目的:
- 为增强降解趋势分析提出一种新的特征融合方法.
- 解决当前关于多传感器数据差异和操作模式正常化方法的局限性.
主要方法:
- 提出了一个基于信号与噪声比度的特征融合方法.
- 慢速特征分析 (SFA) 被利用来量化退化趋势差异,并在特征融合过程中自动过操作模式.
主要成果:
- 拟议的方法有效量化了退化指标 (DIs) 中的退化趋势差异.
- 在特征融合过程中实现了操作模式的自动正常化,提高了预测准确度.
- 在航空发动机和滚动轴承数据集上证明了有效性和优越性.
结论:
- 开发的特征融合方法为预测性维护提供了一种优越的方法.
- 这种方法提高了复杂降解模式的机器预后的可靠性.
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