风力轮机变速箱的故障诊断基于改进的多变量变化模式分解和集成精制的复合多变量多尺度分散透
Xin Xia1, Xiaolu Wang1, Weilin Chen2
1School of Mechanical and Electrical Engineering, Suqian University, Suqian 223800, China.
Entropy (Basel, Switzerland)
|February 26, 2025
概括
这项研究引入了一种改进的风力轮行星变速箱故障诊断方法. 这种新的方法增强了特征提取,并使用先进的信号处理技术实现了更高的诊断准确性.
科学领域:
- 机械工程 机械工程
- 状态监控 状态监控
- 信号处理 信号处理
背景情况:
- 风力轮机行星变速箱是由于复杂的操作条件而容易发生故障的关键部件.
- 有效的故障特征提取和诊断具有挑战性,影响运行可靠性和维护.
- 现有的方法经常与变速箱结构和环境因素的复杂性作斗争.
研究的目的:
- 提高故障特征提取的效率,提高风力轮行星变速箱故障诊断的准确性.
- 提出一种新的故障诊断方法,集成改进的多变量变化模式分解 (IMVMD) 和整体精制的复合多变量多尺度分散 (ERCmvMDE).
- 通过使用多通道振动数据来验证拟议方法的有效性.
主要方法:
- 改进的多变量变化模式分解 (IMVMD) 是为了优化MVMD的参数而开发的,提高了其有效性.
- 集成精制复合多变量多尺度分散 (ERCmvMDE) 用于提取丰富和有效的故障特征信息.
- 最小方位支持向量机 (LSSVM) 用于最终的故障诊断,使用提取的ERCmvMDE特性.
主要成果:
- 拟议的IMVMD方法有效优化了MVMD参数,从而改善了信号分解.
- ERCmvMDE成功地从多通道振动数据中提取了全面和区分的特征.
- 综合IMVMD-ERCmvMDE-LSSVM方法在特征提取和故障诊断准确性方面表现出卓越的性能.
结论:
- 开发的故障诊断方法显著提高了风力轮行星变速箱的特征提取和诊断精度.
- IMVMD和ERCmvMDE的组合为复杂机械的状态监测和故障诊断提供了一个强大的解决方案.
- 这种方法有望提高风力轮机系统的可靠性并降低维护成本.
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