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Temporally preserving Latent Variable Models:Offline and Online Training for Reconstruction and Interpretation of Fault Data for Gearbox Condition Monitoring

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Journal of Dynamics, Monitoring and Diagnostics 2024年第2期3卷 156-177页

作者： Ryan Balshaw p.stephan heyns Daniel N.Wilke stephan Schmidt Center for Asset Integrity Management Department of Mechanical and Aeronautical EngineeringUniversity of PretoriaPretoriaSouth Africa

Latent variable models can effectively determine the condition of essential rotating machinery without needing labeled *** models analyze vibration data via an unsupervised learning *** preservation is necessary to obtain an informative latent manifold for the fault diagnosis *** a temporalpreserving context,two approaches exist to develop a condition-monitoring methodology:offline and *** latent variable models,the available training modes are not *** many traditional methods use offline training,online training can dynamically adjust the latent manifold,possibly leading to better fault signature extraction from the vibration *** study explores online training using temporal-preserving latent variable *** online training,there are two main methods:one focuses on reconstructing data and the other on interpreting the data *** are considered to evaluate how they diagnose faults over *** two experimental datasets,the study confirms that models from both training modes can detect changes in machinery health and identify faults even under varying ***,the complementarity of offline and online models is emphasized,reassuring their versatility in fault *** the implications of the training approach and the available model formulations is crucial for further research in latent variable modelbased fault diagnostics.

关键词： Condition monitoring unsupervised learning latent variable models temporal preservation training approaches

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Keynote Summaries of the First International Symposium on Dynamics,Monitoring,and Diagnostics

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Journal of Dynamics, Monitoring and Diagnostics 2022年第4期1卷 189-199页

作者： JDMD Editorial Office Jérôme Antoni p.stephan heyns Jing Lin Huajiang Ouyang stephan Schmidt Wade A.Smith Daniel N.Wilke Editorial Office of JDMD Chongqing University of TechnologyChongqingChina Laboratoire Vibrations Acoustique Univ LyonINSA LyonLVAEA67769621 VilleurbanneFrance Centre for Asset Integrity Management University of PretoriaPretoriaSouth Africa School of Reliability and Systems Engineering Beihang UniversityBeijingChina School of Engineering University of LiverpoolLiverpoolUK School of Mechanical and Manufacturing Engineering University of New South WalesSydneyAustralia

The first International Symposium on Dynamics,Monitoring,and Diagnostics was held in Chongqing,China,in April *** Symposium,which was attended both virtually and in person,had an audience of 2000 and was aimed at enhancing the intelligence of condition monitoring for engineering *** the Symposium,five keynote addresses were delivered by world leading experts,and this paper is comprised of summaries of these addresses to ensure that the important messages of these speakers are properly on record and readily able to be referenced.

关键词： Eigen-structure assignment gear wear gear diagnostics information theory intelligent diagnosis machinery maintenance passive vibration control prognostics structural modification

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基于时序表征的旋转机械故障诊断方法

基于时序表征的旋转机械故障诊断方法

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第15届全国转子动力学学术大会（ROTDYN2023）

作者：陈岳剑梁喜辉 stephan heyns 牛刚 Ming J.Zuo

旋转机械部件,如齿轮、轴承、转子等,是工业设备的基础部件,其状态监测与故障诊断关乎整体设备的安全可靠运行。本次报告将从状态监测信号的时序依赖关系角度出发,介绍基于时序表征建模的旋转机械故障诊断方法。首先,介绍面向旋转机械... 详细信息

旋转机械部件,如齿轮、轴承、转子等,是工业设备的基础部件,其状态监测与故障诊断关乎整体设备的安全可靠运行。本次报告将从状态监测信号的时序依赖关系角度出发,介绍基于时序表征建模的旋转机械故障诊断方法。首先,介绍面向旋转机械动力学系统辨识的时序表征建模的基本概念;其次介绍基于时序表征模型参数、残差的故障诊断机制

关键词：旋转机械故障诊断时序表变工况

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