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多传感器基于观察者的残留学习与自动变功能在变压下多阶段离心故障诊断的重要性

Saif Ullah¹, Muhammad Farooq Siddique¹, Jong-Myon Kim^2,3

¹Department of Electrical, Electronic, and Computer Engineering, University of Ulsan, Building No. 7, 93 Daehak-ro, Nam-gu, Ulsan, 44610, Republic of Korea.

Scientific reports

|December 16, 2025

概括

这项研究提出了利用传感器融合和自回归建模的离心故障诊断的数据效率框架. 该方法的准确性超过99%,为工业机械健康监测提供了可靠的解决方案.

科学领域:

机械工程机械工程
机器学习机器学习
信号处理信号处理

背景情况:

对于故障诊断的深度学习模型需要广泛的标记数据,这对于离心等工业机器很难获得.
现有的方法与数据稀缺性,不平衡性和高维特征作斗争,阻碍了解释性和效率.
由于设备损坏的风险,收集故障数据具有挑战性.

研究的目的:

为在不同工作压力下开发传感器融合,数据效率高的离心故障诊断框架.
解决深度学习模型在数据要求和特征提取方面的局限性.
提供可靠,可解释和高效的故障诊断解决方案.

主要方法:

使用自回归 (AR) 观察器用于正常类信号建模和在多个传感器上提取故障残留.
计算的统计和光谱描述符 (例如,RMS,频段功率) 来自残余.
使用的自动变换特征 (Auto-PFI) 对缩小维度和特征选择的重要性.
应用高斯混合模型 (GMM) 用于按类的密度估计和故障分类.

主要成果:

在多个压力级别 (3,3.5和4巴) 中实现了超过99%的分类准确性.
与单个传感器设置和现有最先进的方法相比,表现出优越的性能.
使用t-SNE,ROC曲线和混矩阵验证了可靠性和概括性.

结论:

拟议的框架整合了基于AR的剩余建模,Auto-PFI和GMM,提供了一种可靠和可解释的故障诊断方法.
这种方法即使在数据有限或不平衡的情况下也有效,因此适用于现实世界的工业应用.
这种传感器融合的方法提高了离心的诊断准确度和效率.

关键词:

离心的中心离心.错误诊断是一个错误的诊断.高斯混合物模型模型的高斯混合物模型.观察者观察者观察者变特征的重要性振动信号表示振动.

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