使用基于时间域变压器编码器的滚动元件轴承的故障模式分类
Minh Tri Vu1, Motoaki Hiraga2, Nanako Miura2
1Division of Mechanophysics, Graduate School of Science and Technology, Kyoto Institute of Technology, Kyoto 606-8585, Japan.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
本研究介绍了一种用于振动信号分析的变压器模型,在没有数据转换的情况下学习稀疏表示. 这种新的方法提高了使用无监督denoising的故障模式分类,即使在不平衡的数据中也证明有效.
科学领域:
- 机器学习 机器学习
- 信号处理 信号处理
- 机械工程 机械工程
背景情况:
- 变压器模型通常需要数据转换或大量的计算资源.
- 对振动信号的有效分析对于机械健康监测至关重要.
- 现有的方法可能缺乏可解释性和可靠性.
研究的目的:
- 开发一种基于变压器的编码器,用于学习振动信号的稀疏表示.
- 整合无监督的无声化,用于直接的时间域分析.
- 用一个可解释和可信的模型来增强故障模式分类.
主要方法:
- 一个新的变压器编码器架构,集成无监督的无噪声.
- 直接处理振动信号的时间域,避免数据转换.
- 关于IMS和CWRU基准数据集的培训和验证.
主要成果:
- 拟议的模型学习了振动信号的有意义和稀疏的表示.
- 在故障模式分类中取得的竞争性表现,特别是在不平衡的数据集上.
- 通过轻量级架构证明了有效性.
结论:
- 基于变压器的编码器与无监督的消除噪声提供了一个高效和有效的方法来进行振动信号分析.
- 该模型为故障模式分类提供了可解释和可靠的结果.
- 这种方法减少了对数据预处理和大量计算能力的需求.
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