使用潜伏的ODEs和变压器编码器对旋转机械进行基于物理的自我监督诊断
Md Al Amin1, Mohammad Shafat Ahsan2, Jannatul Maua3
1MS in Information Technology, St. Francis College, Brooklyn, New York, United States of America.
PloS one
|February 2, 2026
概括
本研究介绍了一种物理信息自主监督诊断 (PI-SSD) 框架,用于检测旋转机械故障. PI-SSD整合了物理和自我监督的学习,以进行可靠和可转移的故障诊断.
科学领域:
- 机械工程 机械工程
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 旋转机械在工业应用中至关重要,其故障可能会造成重大经济损失.
- 传统的故障检测方法通常需要大型标记数据集,并与域移动作斗争.
- 开发可靠和可解释的故障诊断系统对于预测性维护至关重要.
研究的目的:
- 为旋转机械故障检测提出一个新的物理信息自主监督诊断 (PI-SSD) 框架.
- 利用物理建模和自我监督学习来提高诊断性能和不确定性量化.
- 为了证明框架的有效性和公共数据集上的域名可转移性.
主要方法:
- 开发了一个PI-SSD框架,集成了一个多分辨率卷积编码器,窗口式变压器和带有机械前置的潜在神经ODE模块 (杰夫科特转子动力学).
- 在未标记的健康信号上采用掩面部分重建目标进行自我监督的预训.
- 使用证据分类器对故障概率进行校准不确定性估计.
主要成果:
- 在NASA PHM'09变速箱数据集上获得了最高的宏观F1分数 (0.91) 和最低的预期校准误差 (ECE = 0.022).
- 在没有微调的情况下,在Aalto Rotor数据集 (Macro-F1 = 0.81,PR-MSE = 0.067) 上表现出强大的域移动性能.
- 废弃性研究证实了每个组成部分的贡献,物理一致性分析显示在不同速度下违规率较低.
结论:
- PI-SSD框架为旋转机械故障诊断提供了一个强大的,可解释和可转移的解决方案.
- 将物理知识嵌入自主监督学习中可以提高诊断准确性和可靠性.
- 拟议的方法显示了工业应用的巨大潜力,需要准确的预测性维护.
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