轴承故障特征增强和基于粗粒度格子特征的诊断
Xiaoyu Li1, Baozhu Jia1,2,3, Zhiqiang Liao1,2,3
1Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang 524088, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
这项研究引入了一种新的方法来诊断滚动轴承故障,使用粗粒格子特征 (CGLF) 和Swin变压器网络. 该方法有效地增强了弱故障信号,在识别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
- 人工智能的人工智能
背景情况:
- 滚动轴承是容易发生频繁故障的关键部件.
- 从杂的振动信号中提取故障特征是具有挑战性的.
- 现有的方法在较弱的故障特征和背景噪音方面扎.
研究的目的:
- 提出一种有效的方法来改善和诊断滚动轴承故障.
- 提高在噪音和弱信号的情况下检测故障的准确性.
- 为滚动轴承开发一个强大的故障诊断模型.
主要方法:
- 适应性过以减少振动信号中的背景噪声.
- 频域转换和频谱的粗粒度细分.
- 扩展幅度增强以创建粗粒状格子特征 (CGLF).
- 使用Swin变压器网络和Softmax分类的模式识别.
主要成果:
- 拟议的CGLF方法显著提高了故障特征.
- 基于Swin变压器的模型实现了高故障识别精度 (公开数据集的98.30%,实验室数据的98.50%).
- 该方法在诊断滚动轴承故障方面证明了可行性和有效性.
结论:
- 开发的方法为滚动轴承故障诊断提供了一个高效和可行的解决方案.
- 粗粒格子特征与深度学习相结合,提供了卓越的故障检测.
- 这项研究为机械系统的预测性维护提供了有价值的工具.
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