复合故障特征指数引导变化模式分解与动态加权中央集群用于轴承故障检测
Bangcheng Zhang1, Boyu Shen1, Zhi Gao1
1School of Mechatronic Engineering, Changchun University of Technology, Changchun 130103, China.
本研究引入了一种使用复合故障特征指数 (CFFI) 和光谱能量保留率 (SERR) 的新型轴承故障检测方法. 这种方法提高了旋转机械的故障组件检测,以改善设备监控.
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:
- 旋转机械容易发生轴承故障,导致周期性冲击和共振.
- 现有的检测方法难以处理复杂的故障信号和噪声.
研究的目的:
- 开发用于旋转机械轴承故障的先进检测方法.
- 为了提高断层冲击强度和周期结构的量化.
- 为了提高故障特征保留信号消噪后的准确性.
主要方法:
- 构建了一个复合断裂特征指数 (CFFI),集成正常化曲解和模糊.
- 定义了一种光谱能量保留率 (SERR) 用于评估无声化和特征保留.
- 利用三角形拓聚合优化器 (TTAO) 进行自适应变量模式分解 (VMD) 参数选择.
- 采用动态加权中心集群 (DWCC) 来选内在模式函数 (IMF) 与故障信封信息.
主要成果:
- 拟议的方法在IMS轴承数据集上实现了较高的SERR (0.21356),与实际信号 (0.22465) 相比,相对误差为4.9%,表明重建精度更高.
- 以CFFI为指导的优化有效地增强了冲动和周期性故障组件.
- 在整个消毒过程中,保持了稳定的特征频段保留.
结论:
- 开发的方法通过增强关键故障特征,准确检测轴承故障.
- 这种方法证明了对现实设备监控的强大的工程适用性.
- 这种技术为识别旋转机械中的轴承故障提供了可靠的解决方案.
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