一个多指数的融合适应性化特征提取用于液压轮机的化检测
Yi Wang1, Feng Li1, Mengge Lv1
1Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China.
Entropy (Basel, Switzerland)
|April 26, 2025
概括
在液压轮机中检测洞穴对于效率和寿命至关重要. 一种新的自适应方法改进了化特征的提取,从而以更少的错误报警进行高度准确的检测.
科学领域:
- 机械工程 机械工程
- 流体动力学 流体动力学
- 信号处理 信号处理
背景情况:
- 液压轮机中的洞穴会导致机械损坏,降低效率和寿命.
- 准确的洞穴检测对于操作可靠性和性能至关重要.
- 由于噪音和信号的复杂性,提取化特征是很困难的.
研究的目的:
- 提出一种适应性化特征提取和检测的新方法.
- 为了克服水声信号中噪声和信号非静止所带来的挑战.
- 提高液压轮机中空洞化监测的准确性和可靠性.
主要方法:
- 开发了一个多指数融合自适应变化模式分解 (VMD) 算法.
- 使用空洞化指标,通过适应性来确定VMD分解层的数量.
- 洞穴特征是根据不同洞穴度的频率特征来选择的.
主要成果:
- 拟议的方法有效地保留了洞穴信息,并提高了特征提取质量.
- 成功检测到初始和超化.
- 实验验证表明检测准确度高,误报率低.
结论:
- 多指数融合自适应方法为液压轮机中空洞化检测提供了强大的解决方案.
- 这种方法提高了运行可靠性,并保持了能源转换效率.
- 这些发现对于液压机械的预测性维护和性能优化具有重要意义.
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