精炼复合材料移动平均波动分散和其在滚动轴承故障诊断上的应用
Yinsheng Chen1, Jiahui Chen1, Yukang Qiang1
1School of Measurement and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China.
The Review of scientific instruments
|October 11, 2023
概括
一种新的方法,精炼复合材料移动平均波动分散 (RCMAFDE),通过提供稳定的值来增强旋转机械故障诊断. 这种技术,结合一个混乱子搜索优化算法优化的内核极端学习机器,在识别滚动轴承故障方面实现了高精度.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 数据科学数据科学数据科学
背景情况:
- 多尺度分析对于旋转机械故障诊断至关重要.
- 由于时间序列缩短,传统的粗粒法遭受不稳定的值和不断增加的尺度因子的影响.
- 这种不稳定性限制了现有方法在断层特征提取中的应用.
研究的目的:
- 引入一种新的特征提取方法,精制复合材料移动平均波动分散 (RCMAFDE),用于振动信号.
- 通过将RCMAFDE与优化的内核极端学习机器 (KELM) 集成,开发一套针对滚动轴承的强大的故障诊断方法.
- 解决现有方法在处理时间序列长度变化和干扰方面的局限性.
主要方法:
- 开发和模拟RCMAFDE用于跨不同时间序列长度和干扰的稳定计算.
- 使用RCMAFDE从预处理的振动信号中提取故障特征向量.
- 整合RCMAFDE与混乱子搜索优化算法 (CSSOA) 优化的KELM进行故障诊断模型培训和测试.
主要成果:
- 在模拟实验中,RCMAFDE在不同的时间序列长度和干扰下证明了值的高稳定性.
- 提出的基于RCMAFDE的故障诊断方法在多个数据集中实现了最高的故障识别准确性.
- 该方法有效地从振动信号中提取非线性动态复杂性信息,优于比较方法.
结论:
- RCMAFDE提供了一种稳定有效的方法,用于在振动信号中提取故障特征.
- 结合的RCMAFDE和CSSOA-KELM方法为滚动轴承故障诊断提供了卓越的解决方案.
- 拟议的技术提高了旋转机械故障识别的准确性和可靠性.
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