一种用于自适应性地分离和提取内轮电机轴承复合故障特征的新方法
Yukun Tao1, Chun Ge1, Han Feng1
1School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China; International Joint Laboratory on Mobility Equipment and Artificial Intelligence for IT Operations, Zhenjiang 212013, China.
ISA transactions
|February 4, 2025
概括
这项研究引入了一种新方法,通过自适应地分离信号和提取特征来检测车轮内电机轴承的复合故障. 这种方法增强了弱故障信号,以提高检测准确度.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 状态监控 状态监控
背景情况:
- 车轮内电机轴承的复合故障对准确检测提出了重大挑战.
- 传统的方法与弱故障特征提取和多源信号干扰作斗争.
研究的目的:
- 开发一种用于适应性分离多源信号和提取内轮电机轴承中复合故障特征的新方法.
- 通过整合盲目解卷技术来增强弱断层特征的提取.
主要方法:
- 盲源分离 (BSS) 集成与盲解卷.
- 使用局部平均分解来解决BSS未确定条件的自适应信号重建.
- 精制的非负矩阵分解 (NMF) 使用伊塔库拉-赛托距离和稀疏约束.
- 使用信封频谱峰值因子进行自适应故障源信号识别.
- 改进的多点最佳最小解卷调整 (MOMEDA) 波形延伸以减少终点效应.
主要成果:
- 成功分离多源信号并提取复合故障特征.
- 增强了弱故障特征的表达和提取.
- 通过模拟和实验验证拟议方法的有效性和稳定性.
结论:
- 拟议的自适应方法有效地检测轮内电机轴承的复合故障.
- 该方法在各种工作条件和故障类型中显示出稳定性.
- 整合BSS,盲目解卷和自适应重建为状态监测提供了一个强大的工具.
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