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滚动轴承的剩余使用寿命预测基于CBAM-CNN-LSTM

Bo Sun¹, Wenting Hu¹, Hao Wang¹

¹School of Mechanical and Vehicle Engineering, Changchun University, Changchun 130022, China.

Sensors (Basel, Switzerland)

|January 25, 2025

概括

本研究介绍了一种使用卷积神经网络 (CNN) 和长短期记忆 (LSTM) 网络的卷积块注意力模块 (CBAM) 的新的剩余有用寿命 (RUL) 预测方法. 拟议的方法显著提高了设备的RUL预测准确性和可靠性.

科学领域:

机械工程机械工程
人工智能的人工智能

背景情况:

预测剩余使用寿命 (RUL) 对设备安全和运营效率至关重要.
传统的方法很难有效地利用复杂的时间序列数据来准确预测RUL.
卷积神经网络 (CNN) 在RUL预测任务中经常无法最佳地利用空间和通道特征.

研究的目的:

开发一个先进的RUL预测模型,增强特征提取和时间学习.
解决标准CNN在捕获其余寿命评估的基本数据特征方面的局限性.
提高机械元件中RUL预测的准确性和可靠性.

主要方法:

利用快速里埃转换 (FFT) 将时间域数据转换为频域.
使用卷积神经网络 (CNN) 来从频域数据中提取初始特征.
集成了卷积块注意模块 (CBAM),以有效地权衡频道和空间特征.
整合了长期短期记忆 (LSTM) 网络,以从注意力加权特征中学习时间依赖.

主要成果:

拟议的模型在PHM2012轴承数据集上表现出卓越的性能.
预测错误的显著减少:平均平方误差 (MSE) 53%,平均绝对误差 (MAE) 16.87%,根平均平方误差 (RMSE) 31.68%.
在各种故障模式中表现出强大的RUL预测准确度.

结论:

新的CBAM增强的CNN-LSTM模型为RUL预测提供了一种优越的方法.
该方法有效地整合了频率域分析,注意力机制和时间学习.
这些发现证实了该模型的有效性和预测性维护中的真实应用潜力.

关键词:

卷积块注意力模块的卷积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.预测剩余的使用寿命滚动轴承滚动轴承滚动轴承

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