振动和散流信号融合用于检测滚动轴承中的腐蚀损伤,使用集成学习算法
José Pablo Pacheco-Guerrero1, Israel Zamudio-Ramírez1, Larisa Dunai2
1Engineering Faculty, San Juan del Río Campus, Universidad Autónoma de Querétaro, Av. Río Moctezuma 249, San Juan del Río 76807, Querétaro, Mexico.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
在感应电机中早期检测轴承腐蚀对于运行效率和降低成本至关重要. 这项研究引入了一种使用磁流和振动分析的新方法,在诊断腐蚀方面达到99%以上的准确性,即使在早期阶段也是如此.
科学领域:
- 机械工程 机械工程
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
背景情况:
- 感应电机是重要的工业部件,但轴承腐蚀,通常是由湿度和热量引起的,导致性能降低和昂贵的故障.
- 由于轴承腐蚀的扩散性质,早期诊断具有挑战性,与传统光谱分析可以检测到的局部故障不同.
- 这种缺乏有效的诊断方法需要新的方法来防止意想不到的关闭和降低维护成本.
研究的目的:
- 开发和验证一种可靠的方法,用于在感应电机中早期诊断轴承腐蚀的故障.
- 探索磁流浪流和振动信号分析用于检测扩散腐蚀的有效性.
- 通过先进的信号处理和机器学习技术,提高诊断准确性和稳定性.
主要方法:
- 在不同程度的轴承腐蚀下分析磁流和振动信号.
- 使用统计和非统计参数来捕捉运动动态行为变化.
- 采用遗传算法来进行特征选择和集体学习模型优化.
- 实现一个支持向量机 (SVM) 与袋装方法,以进行可靠的分类.
主要成果:
- 提出的方法成功地确定了不同程度的轴承腐蚀.
- 实现了超过99%的分类准确度,以区分健康状态和腐蚀状态.
- 证明了结合磁流流量,振动分析和集体学习方法的可靠性和效率.
结论:
- 开发的方法提供了一种可靠和有效的解决方案,用于在感应电机中早期诊断轴承腐蚀的故障.
- 信号分析,遗传算法和集体学习的整合显著提高了诊断准确度.
- 这种方法提供了一个有前途的策略,以减轻因轴承腐蚀导致的感应电机故障所造成的经济损失.
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