在复杂的操作条件下识别船舶滚动轴承故障:基于多域特征提取的LCM-HO增强的LSSVM方法
Qiang Yuan1, Jinzhi Peng1, Xiaofei Wen1
1School of Naval Architecture and Maritime, Zhejiang Ocean University, Zhoushan 316022, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
一种新方法将最小平方支向量机 (LSSVM) 与改进的河马优化 (LCM-HO) 结合起来,提高了海洋轴承故障诊断. 这种方法实现了高精度,为复杂的海洋系统提供了强大的解决方案.
科学领域:
- 海洋工程 海洋工程
- 机械诊断 机械诊断 机械诊断
- 人工智能的人工智能
背景情况:
- 由于合振动和复杂的操作条件,海洋推进系统在轴承故障诊断方面存在独特的挑战.
- 传统的方法与现代海洋设备的复杂性作斗争,需要先进的诊断方法.
研究的目的:
- 开发和验证一种用于船舶轴承的新型故障诊断方法.
- 解决复杂海洋环境中现有技术的局限性.
主要方法:
- 一种混合方法,集成多域特征提取 (时间,光谱,时间频率) 与最小平方支持向量机 (LSSVM).
- 改进的河马优化算法 (LCM-HO) 用于功能优化.
- 从原始信号中直接提取特征,以简化预处理.
主要成果:
- 拟议的LCM-HO-LSSVM方法在Paderborn轴承数据集上实现了99.11%的高诊断准确率,在自制的海洋轴承试验台上达到98.00%.
- 与现有方法相比,表现出显著的性能改进.
结论:
- 在具有挑战性的海洋环境中,LCM-HO-LSSVM方法为轴承故障诊断提供了可靠,高效和强大的解决方案.
- 该技术显示出在海洋设备维护中的实际实地工程应用的巨大潜力.
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