基于富里埃贝塞尔EWT和多路调节ELM的变速箱故障诊断
1Faculty of Artificial Intelligence and Big Data, Yibin University, Yibin, 644000, China.
Scientific reports
|September 2, 2023
概括
一种新的变速箱故障诊断方法使用富里埃贝塞尔数列扩展型实证波纹变换 (FBEWT) 和多重规则化极端学习机器 (MRELM) 以获得更高的准确性. 这种方法有效地识别了均衡和不均衡数据集中的变速箱故障.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 变速箱故障诊断对于工业机械维护至关重要.
- 传统的方法难以处理复杂的振动信号和不平衡的数据.
- 准确的故障检测提高了运营安全,减少了停机时间.
研究的目的:
- 为变速箱提出一种新而强大的故障诊断方法.
- 为了增强来自变速箱振动信号的特征提取.
- 提高故障检测系统的诊断准确性和稳定性.
主要方法:
- 使用富里埃贝塞尔数列扩展型实证波纹变换 (FBEWT) 进行非静止特征提取.
- 实施多重规范化极端学习机器 (MRELM) 进行分类.
- 在平衡和不平衡数据集上验证方法.
主要成果:
- 与FBEWT-ELM,EWT-MRELM和EWT-ELM相比,提出的FBEWT-MRELM方法显示出更高的性能.
- 该方法在不同的数据集条件下取得了出色的故障诊断结果.
- 在识别变速箱故障时观察到高稳定性和准确性.
结论:
- 在变速箱故障诊断方面,FBEWT-MRELM提供了显著的进步.
- 该方法有效地捕获了必要的信号特征,以改善检测.
- 这种技术为现实世界变速箱监控提供了稳定而准确的解决方案.
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