应用FCEEMD-TSMFDE和自适应CatBoost在复杂可变条件轴承的故障诊断中的应用
Min Mao1, Bingwei Xu2, Yuhuan Sun3
1Faculty of Information Engineering, Quzhou College of Technology, Quzhou, 324000, China.
Scientific reports
|December 16, 2024
概括
这项研究引入了一种新的故障诊断方法,使用先进的信号处理和机器学习来准确识别轴承故障. 该方法通过增强特征提取和自适应模型优化,显著提高诊断准确性.
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:
- 现有的快速组合实证模式分解 (FEEMD) 方法存在模式混合和选择偏差,阻碍了有效的故障组件提取.
- 多尺度模糊分散 (MFDE) 可以失去粗粒度信息,降低故障特征的稳定性.
- 标准的CatBoost模型具有有限的适应性,影响轴承故障诊断的准确性.
研究的目的:
- 为复杂的操作条件提出一种新的故障诊断方法.
- 为了提高轴承故障诊断的准确性和稳定性.
- 克服现有的信号处理和机器学习技术的局限性.
主要方法:
- 快速互补组合实证模式分解 (FCEEMD) 与配对的白噪声来抑制模式别名.
- 使用最大信息系数/吉尼指数选择相关内在模式函数 (IMF) 组件的复合选策略.
- 时间转移多尺度模糊分散 (TSMFDE) 用于完全和稳定的断裂特征提取.
- 适应性Optuna-CatBoost通过超参数优化进行准确的轴承故障诊断.
主要成果:
- 拟议的FCEEMD方法有效地分解非别名振动模式,改善信号质量.
- 复合选策略可以过不相关的噪音模式.
- TSMFDE成功地提取了稳定的故障特征.
- 适应性Optuna-CatBoost模型实现了平均故障诊断准确率为99.76%和99.33%.
结论:
- 集成的FCEEMD-TSMFDE-Optuna-CatBoost方法为复杂的操作条件故障诊断提供了一个强大的解决方案.
- 提出的技术显著提高了轴承故障识别的准确性和稳定性.
- 该模型显示了在特征提取和模式识别方面更广泛应用的潜力.
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