混合式多模式功能融合与多传感器用于轴承故障诊断
Zhenzhong Xu1, Xu Chen2, Yilin Li3
1College of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, China.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
这项研究引入了一种新的多传感器故障诊断算法,用于轴承,在噪音条件下提高准确性. 混合式多式联动功能融合方法在识别轴承故障方面取得了97.54%的成功率.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 传统的单传感器振动分析不足以进行全面的轴承状态评估,尤其是在高噪音环境中.
- 现有的故障特征提取算法在复杂的操作条件和噪声干扰中扎.
研究的目的:
- 开发一种高精度轴承故障诊断算法,使用多传感器数据和混合多式联络功能融合.
- 提高故障检测在具有挑战性,高噪音的工业环境中的稳定性和准确性.
主要方法:
- 使用主要组件分析 (PCA) 的水平和垂直振动信号的融合.
- 通过连续波形变换 (CWT) 来生成时间频率特征图.
- 开发一种双模型方法,将剩余神经网络 (ResNet) 和支持矢量机器 (SVM) 结合起来,通过合体学习进行集成,并通过遗传算法 (GA) 进行优化.
主要成果:
- 拟议的算法实现了97.54%的诊断准确率.
- 混合多式联通功能融合有效地利用多传感器信息来改善状态表示.
- 合体学习和GA优化显著提高了最终模型的诊断性能.
结论:
- 多传感器和混合多模式功能融合方法为轴承故障诊断提供了卓越的性能.
- 集成先进的信号处理和机器学习技术为高噪音环境提供了强大的解决方案.
- 这种方法对于需要可靠的条件监测的现实世界工业应用具有显著的潜力.
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