基于模拟和实验融合驱动器的滚动轴承故障诊断方法的研究
Yonghua Li1,2, Denglong Wang1, Xin Zhao1
1College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China.
The Review of scientific instruments
|June 12, 2024
概括
这项研究引入了一种用于滚动轴承的新型故障诊断方法,融合模拟和实验数据以提高准确性. 该方法有效地解决了轴承故障检测中有限数据和信号不确定性的挑战.
科学领域:
- 机械工程 机械工程
- 状态监控 状态监控
- 人工智能的人工智能
背景情况:
- 滚动轴承故障数据很少,阻碍了传统的诊断方法.
- 现有的方法往往忽略信号不确定性,并表现出较低的准确性.
- 面对数据限制和信号变化,需要进行强大的故障诊断.
研究的目的:
- 通过模拟和实验数据融合,提出用于滚动轴承的故障诊断方法.
- 解决轴承故障诊断中数据稀缺和信号不确定性的挑战.
- 提高滚动轴承故障检测模型的准确性和可靠性.
主要方法:
- 在各种故障条件下建立了用于滚动轴承的动力学模拟模型.
- 利用一个序列生成对抗网络 (SeqGAN) 来融合模拟和实验数据.
- 构建了概率盒模型以捕捉故障信号的不确定性,并提取了特征向量.
- 采用极端梯度提升树 (XGBoost) 模型进行故障诊断分类.
主要成果:
- 与融合前方法相比,数据融合方法显著改善了分类和诊断效应.
- 概率盒模型有效地描述了轴承振动信号的不确定性.
- 提出的方法证明了良好的诊断效果,即使数据不足.
结论:
- 模拟和实验融合驱动方法适用于滚动轴承故障诊断.
- 数据融合和不确定性建模的整合提高了诊断准确度.
- 这种方法在数据稀缺的情况下为故障诊断提供了可行的解决方案.
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