一个通过aquila优化算法优化的自适应性层次混合内核ELM,用于轴承故障诊断
Hao Yan1, Liangliang Shang2, Wan Chen1
1School of Electrical Engineering and Automation, Nantong University, Jiangsu, 226001, China.
Scientific reports
|April 8, 2025
概括
本研究介绍了一种智能方法,用于诊断滚动轴承故障,使用堆叠的无音自动编码器 (SDAE) 和自适应的层次混合内核极端学习机器 (AHHKELM). 拟议的SDAE-AHHKELM方法实现了卓越的故障分类准确性和稳定性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 滚动轴承对于旋转机械至关重要,影响经济和社会系统.
- 有效的故障诊断对于保持运营完整性和防止故障至关重要.
- 现有的方法可能会与复杂的故障模式和数据噪声作斗争.
研究的目的:
- 开发一种先进的智能方法,用于准确的滚动轴承故障诊断.
- 在轴承故障检测中提高分类准确性和模型稳定性.
- 为了提高诊断性能,利用深度学习和优化机器学习.
主要方法:
- 使用堆叠无声自动编码器 (SDAE) 进行自动深度特征提取.
- 实现一个自适应的等级混合内核极端学习机器 (AHHKELM) 进行分类.
- 使用一个增强的Aquila优化器 (AO) 与混乱映射用于超参数优化.
主要成果:
- 与传统方法相比,SDAE-AHHKELM方法显示出更高的故障分类准确性.
- 在CWRU,MFPT和JNU数据集上的实验验证证证了该模型的稳定性和概括能力.
- 混合内核方法有效地捕获了线性和非线性数据模式.
结论:
- 拟议的SDAE-AHHKELM为智能轴承故障诊断提供了一个高度有效的解决方案.
- 整合SDAE和AHHKELM显著提高了诊断性能.
- 增强的AO算法为极端学习机器模型提供了强大的参数优化.
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