基于EBKA-TVF-EMDD的振动选激发平台的集成加速对位移的研究
1College of Mechanical and Automotive Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou, 310018, China.
Heliyon
|February 11, 2025
概括
本研究引入了一种增强的算法 (EBKA-TVF-EMD),用于精确测量振动位移. 该方法提高了加速信号重建的准确性,这对于工程应用至关重要.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 优化算法 优化算法
背景情况:
- 精确的位移测量对于振动选平台至关重要.
- 传统的加速度集成方法可能会出现累积的错误.
- 现有的信号分解技术可能会与噪声和复杂的信号趋势作斗争.
研究的目的:
- 提出并验证一种增强的信号预处理方法,用于准确计算振动位移.
- 为了提高从噪音数据中重建加速度信号的准确性.
- 为了实现对振动选激发平台的精确位移测量.
主要方法:
- 开发了一种增强的黑翼风算法 (EBKA),将Gompertz细菌生长模型用于动态步骤大小调整.
- 利用EBKA优化基于时间变异过的实证模式分解 (TVF-EMD) 的参数.
- 通过重建加速信号的频域集成 (FDI) 获得的振动位移.
主要成果:
- 与传统算法相比,EBKA表现出卓越的全球搜索能力和融合稳定性.
- 提出的EBKA-TVF-EMD方法显著减少了与传统方法 (TVF-EMD,PSO-TVF-EMD) 相比的重建错误.
- 实验验证显示,在振动选平台上的位移测量中,相对误差仅为1.75%.
结论:
- 该EBKA-TVF-EMD方法有效地提高了加速信号分解的准确性.
- 这种方法满足了精确测量振动位移的工程要求.
- 这种优化的分解和集成技术为动态系统分析提供了强大的解决方案.
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