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滚动轴承剩余使用寿命预测基于CNN-VAE-MBiLSTM的数据

Lei Yang¹, Yibo Jiang², Kang Zeng³

¹The ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China.

Sensors (Basel, Switzerland)

|May 25, 2024

概括

这项研究引入了一种新的CNN-VAE-MBiLSTM模型,用于准确预测滚动轴承的剩余使用寿命 (RUL). 该模型通过在工业环境中提高RUL预测准确性和稳定性来增强维护.

科学领域:

机械工程机械工程
人工智能的人工智能
预测性维护是指预测性维护.

背景情况:

预测滚动轴承的剩余使用寿命 (RUL) 对于防止设备故障和优化维护计划至关重要.
现实世界的工业环境面临信号干扰和过程复杂性等挑战,阻碍了准确的RUL预测.
现有的方法经常在特征提取和捕获多轴传感器数据中复杂的时间依赖性方面扎.

研究的目的:

为滚动轴承提出一种新而强大的剩余使用寿命 (RUL) 预测模型.
利用深度学习技术进行自动特征提取和顺序数据分析.
提高复杂工业机械中RUL预测的准确性和可靠性.

主要方法:

一个混合深度学习模型,CNN-VAE-MBiLSTM,集成卷积神经网络 (CNN),变化自编码器 (VAE) 和多个双向长期短期记忆 (MBiLSTM) 网络.
利用CNN-VAE从多轴信号的时间频谱中自动提取低维特征.
采用MBiLSTM来捕获顺序特征和轴间特征差异,以准确预测RUL.

主要成果:

在CNN-VAE组件有效地提取突出的特征,减少维度和设计师偏见.
MBiLSTM组件通过分析提取的特征及其跨多个轴的时间依赖,准确地预测RUL.
与现有方法相比,在一个工业案例上进行的验证显示出更高的准确性和抗噪声能力.

结论:

拟议的CNN-VAE-MBiLSTM模型在滚动轴承RUL预测方面取得了重大进展.
整合CNN-VAE和MBiLSTM提供了强大的特征提取和准确的RUL预测.
这种方法有望提高滚动机械预测性维护策略的可靠性和效率.

关键词:

双向的长期短期记忆.剩余的使用寿命.变量自动编码器变量自动编码器

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