基于改进的多尺度转换 entropy 的曲轴故障诊断方法的研究
Fengfeng Bie1, Yu Shu1, Fengxia Lyu1
1School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
一种使用遗传算法优化多尺度变量的新方法改善了曲轴振动分析. 这种方法通过提供强大的和可适应的冲击特征提取来增强反转机械系统的故障检测.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 数据分析 数据分析
背景情况:
- 曲轴监控对于反转机械至关重要.
- 曲轴的断裂振动是非静止和非线性,挑战传统的方法.
- 单个尺度的特征提取不足以进行复杂的故障评估.
研究的目的:
- 提出一种针对曲轴振动的新型撞击特征提取方法.
- 提高传输系统故障诊断的准确性和稳定性.
- 解决分析复杂振动信号的单级方法的局限性.
主要方法:
- 改进了完整的集体实证模式分解与自适应噪声 (ICEEMDAN) 进行信号分解.
- 基因算法 (GA) 优化多尺度转换 (MPE) 参数.
- 粒子集群优化 (PSO) 为模式识别优化了支持向量机 (SVM).
主要成果:
- 拟议的GA优化MPE方法与ICEEMDAN和PSO-SVM相结合,证明了卓越的性能.
- 该方法在处理曲轴振动信号方面显示出高强度和适应性.
- 实现了有效的冲击特征提取和准确的模式识别.
结论:
- 开发的方法在曲轴故障诊断方面取得了重大进展.
- 使用GA优化MPE提供了一种更有效的方法来分析复杂的振动数据.
- 这种技术通过改进监控,提高了机械系统的可靠性.
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