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Updated: Apr 30, 2026

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Published on: October 1, 2019
使用DTCWT-RCMFDE和LSSVM算法诊断错位故障
Ahmed Taibi1, Nabil Ikhlef1, Lyes Aomar1
1Electronics and Industrial Electrical Laboratory (L2EI), Univercity of Jijel, Jijel, Algeria.
这项研究引入了一种使用双树复杂波形变换和精细复合多尺度波动分散 (DTCWT-RCMFDE) 与最小方位支向量机 (LSSVM) 的新方法,以准确检测电机失调. DTCWT-RCMFDE-LSSVM模型实现了98.33%的准确性,显著提高了旋转电机的诊断性能.
科学领域:
- 工程
- 电气工程
- 机械工程
背景情况:
- 错位是旋转电机常见的机械故障,导致潜在的电机故障.
- 对于工业可靠性和尽量减少停机时间,早期和准确的检测是非常重要的.
研究的目的:
- 提出一种创新的方法来诊断旋转电机的错位故障.
- 提高工业旋转机器故障检测的准确性和效率.
主要方法:
- 该研究将双树复杂波形变换 (DTCWT) 与精细的复合多尺度波动分散 (RCMFDE) 算法集成为特征提取.
- 最小方形支向量机 (LSSVM) 算法用于根据提取的特征对错错位的分类.
- 用范围 (RE) 进行详细分析,将扭矩信号分解为子频段.
主要成果:
- 拟议的DTCWT-RCMFDE-LSSVM模型的分类精度达到了98.33%,比现有方法更高.
- 该方法在识别平行和角度错位方面表现出卓越的诊断性能.
- 实验验证证了拟议的故障诊断方法的高精度和有效性.
结论:
- DTCWT-RCMFDE-LSSVM方法为检测旋转电机的错位提供了一个非常准确和可靠的解决方案.
- 这项研究对工业应用具有重大潜力,提高了发电和制造业等行业的运行可靠性.
- 这些发现强调了先进的信号处理和机器学习技术对预测性维护的重要性.
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