基于RQA与STD和WOA-SVM的滚动轴承故障诊断
Wentao Qiu1, Bing Wang1, Xiong Hu1
1Shanghai Maritime University, Shanghai 201306, China.
Heliyon
|February 29, 2024
概括
本研究介绍了一种使用递归定量分析 (RQA) 和鱼优化算法支持矢量机器 (WOA-SVM) 的新型滚动轴承故障诊断方法. 这种先进的技术在识别轴承故障方面取得了很高的准确性,提高了工业安全.
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:
- 滚动轴承是工业机械的关键部件,它们的故障会导致严重的操作中断和安全危险.
- 准确和及时的故障诊断对于预测性维护和防止灾难性故障至关重要.
- 现有的故障诊断方法可能缺乏复杂工业环境所需的准确性或稳定性.
研究的目的:
- 为滚动轴承提出一种新且高度准确的故障诊断方法.
- 将非线性时间序列分析与先进的机器学习相结合,以提高诊断性能.
- 用现实世界的滚动轴承数据集来验证拟议的方法.
主要方法:
- 使用递归定量分析 (RQA) 进行振动信号分析,从递归图中提取非线性特征.
- 通过将RQA参数与标准偏差 (STD) 结合起来来生成特征向量.
- 使用鱼优化算法 (WOA-SVM) 优化支向量机 (SVM) 模型参数 (c 和 g).
主要成果:
- 拟议的WOA-SVM与RQA和STD相结合,在Case Western Reserve大学数据集上实现了100%的故障识别准确性.
- 该方法在江南大学滚动轴承数据集上显示了95.00%的准确性.
- 开发的技术在诊断准确性和适用性方面明显优于现有方法.
结论:
- 拟议的基于RQA和WOA-SVM的方法为滚动轴承故障诊断提供了强大而准确的解决方案.
- 这种方法在工业环境中具有广泛的实用性,有助于加强安全和环境政策.
- 该研究成功地解决了当前机械故障诊断技术的局限性.
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