超声波共振研究和因旋转轴承系统的斜裂纹引起的故障诊断
Weipeng Sun1, Kaicheng Zhang1, Shen Hu2
1Institute of Water Resources and Hydro-electric Engineering, Xi'an University of Technology, Xi'an, 710048, China.
ISA transactions
|August 9, 2025
概括
快速诊断转子裂对于安全至关重要. 这项研究模型破解故障,识别共振特征,并使用优化的神经网络进行准确的故障分类,Kernel极端学习机显示最佳效率.
科学领域:
- 机械工程 机械工程
- 旋转机动力学 旋转机动力学
- 错误诊断 错误诊断 错误诊断 在
背景情况:
- 转子轴承系统容易发生裂纹故障,造成重大安全风险.
- 早期发现和诊断这些裂对于防止灾难性故障至关重要.
- 现有的诊断方法在识别复杂的裂情景时可能缺乏准确性或效率.
研究的目的:
- 研究转子轴承系统中裂纹故障的机制.
- 开发一种诊断方法,区分裂纹故障和其他问题.
- 评估不同机器学习模型的性能,以对裂故障进行分类.
主要方法:
- 开发了一个裂纹刚度模型,使用断裂力学进行应力分析.
- 使用多故障旋转机测试台进行实验验证.
- 采用波共振特征用于故障识别.
- 使用Sparrow Search Algorithm (SSA) 应用和优化了反向传播 (BP),内核极端学习机器 (KELM) 和随机森林 (RF) 神经网络.
主要成果:
- 对于显著的裂深度,临界旋转速度随着裂角度的增加而降低.
- 观察到超声波和水平共振与歇斯底里峰值在一个斜裂转子.
- 这三个优化的神经网络都实现了对转子故障的高分类准确性.
- 通过Sparrow搜索算法优化的内核极端学习机器展示了卓越的效率.
结论:
- 开发的裂纹刚度模型准确地代表了旋转器的行为.
- 波共振特征是裂纹断层的标志.
- 优化的神经网络,特别是KELM,为旋翼裂故障诊断提供了有效和高效的解决方案.
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