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一个新的深度学习框架,用于提高滚动轴承故障诊断,使用VAE增强的CNN模型

Yu Wang¹, Dexiong Li¹, Lei Li¹

¹Department of Electrical Engineering, Shijiazhuang Institute of Railway Technology, Shijiazhuang, 050041, China.

|August 21, 2024

概括

本研究引入了一种深度学习模型,将变化自编码器 (VAE) 和卷积神经网络 (CNN) 结合起来,用于增强滚动轴承故障诊断. VAE-CNN模型显著提高了机械设备从振动信号中检测故障的准确性和稳定性.

科学领域:

机械工程机械工程
人工智能的人工智能
信号处理信号处理

背景情况:

滚动轴承是工业机械的关键部件,其运行完整性直接影响设备的整体性能.
有效的故障诊断对于工业环境中滚动轴承的持续监测和维护至关重要.
传统的故障检测方法与杂,复杂的振动数据作斗争,导致精度和可靠性降低.

研究的目的:

通过深度学习开发用于滚动轴承的创新故障诊断方法.
通过解决振动信号分析方面的挑战,提高机械设备故障检测的准确性和稳定性.
通过提取详细的振动信号特征,精确识别和分类滚动轴承故障.

主要方法:

这是一种深度学习方法,它结合了变量自编码器 (VAE) 来实现噪声强度和卷积神经网络 (CNN) 来实现特征表达.
使用重组参数化技巧来进行未经监督的潜在特征学习.
结合自适应值方法,3/5"策略和放弃模型优化.

主要成果:

VAE-CNN模型在不同旋转速度的各种故障类型中实现了超过90%的诊断准确度.
实验验证表明,与没有VAE增强的传统深度神经网络模型相比,其性能优越.
该模型在滚动轴承故障诊断的准确性和稳定性方面显著改进.

结论:

拟议的VAE-CNN模型为工业环境中的滚动轴承故障诊断提供了强大而可靠的解决方案.
深度学习,特别是VAE-CNN架构,有效地解决了用于机械健康监测的振动信号分析的复杂性.
这种方法显著提高了故障检测的精度和可靠性,有助于改善工业设备的维护.

关键词:

卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.错误诊断是一个错误的诊断.滚动轴承是一个滚动轴承.变量自动编码器变量自动编码器

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