贝叶斯优化混合内核SVM用于滚动轴承故障诊断
Xinmin Song1, Weihua Wei2, Junbo Zhou1
1College of Engineering, Nanjing Agricultural University, Nanjing 210031, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
我们开发了一种用于滚动轴承的新故障诊断模型,使用了通过贝叶斯优化 (BO) 优化的混合内核支向量机 (SVM). 这种方法显著提高了故障识别准确度,从85%提高到100%.
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:
- 滚动轴承是机械的关键部件,其故障可能导致重大经济损失.
- 准确的故障诊断对于预测性维护和防止灾难性故障至关重要.
- 传统的方法在与来自轴承故障的振动信号的非线性和非静态性作斗争.
研究的目的:
- 为滚动轴承提出一种新的故障诊断模型.
- 为了应对在轴承振动信号中模两可的故障识别的挑战.
- 提高轴承故障诊断的准确性和可靠性.
主要方法:
- 使用离散里埃转换 (DFT) 来从振动信号中提取特征.
- 开发一个混合内核支持向量机 (SVM),结合多项式和辐射基函数.
- 使用贝叶斯优化 (BO) 优化SVM参数以提高性能.
- 使用Case Western Reserve大学轴承数据集和实验室实验验的验证.
主要成果:
- 拟议的模型在Case Western Reserve大学的数据集上实现了100%的故障诊断准确度.
- 与直接SVM输入 (85%准确度) 相比,优化模型显示了显著的改进.
- 实验室验证表明100%的准确性,在五个复制品中平均为96.7%.
- 贝叶斯优化的混合内核SVM在准确性方面超过了其他诊断模型.
结论:
- 提出的故障诊断模型对滚动轴承具有高度有效性和优越性.
- 混合内核SVM和贝叶斯优化的结合为复杂的故障识别提供了强大的解决方案.
- 该方法显示了在预测性维护中的工业应用的巨大潜力.
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