一种新型智能故障诊断方法用于自离心的自
Bo Zhang1, Zhenya Wang2, Ligang Yao1
1School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.
Entropy (Basel, Switzerland)
|November 24, 2023
概括
这项研究提出了一种智能方法,用于诊断自启动离心的故障. 这种新的方法在识别故障方面达到100%的准确性,明显优于现有技术.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 对自式离心的实时诊断监测对于运行安全至关重要.
- 由于的复杂性,现有的故障诊断方法在特征提取和准确的故障识别方面扎.
研究的目的:
- 引入智能故障诊断方法,用于自动启动的离心.
- 为了应对特征提取和分类准确性的挑战,用于故障诊断.
主要方法:
- 利用精细的时移多尺度波动分散来进行强大的特征提取.
- 雇佣的同位素对向受约束的监督分流映射用于故障特征的维度缩小.
- 应用了适应性混乱的Aquila优化支持矢量机器,用于智能故障分类.
主要成果:
- 拟议的方法实现了100%的识别率,用于各种故障类型的自启动离心.
- 与五种现有的智能故障诊断技术相比,表现出优异的性能.
- 与现有方法相比,实现了平均正确识别率增加15.97%.
结论:
- 开发的智能故障诊断方法对自启动离心非常有效.
- 基于的特征提取,多元组映射和优化的SVM的组合显著提高了故障识别准确性.
- 这种方法为离心的可靠实时监控和诊断提供了一个有希望的解决方案.
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