基于CAE和AGRU的滚动轴承退化趋势预测

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (12) : 109-117.

论文

焦玲玲1，陈捷1,2，刘连华1

作者信息 +

Degradation trend prediction of rolling bearings based on CAE and AGRU

JIAO Lingling1，CHEN Jie1,2，LIU Lianhua1

Author information +

文章历史 +

摘要

针对旋转机械中滚动轴承退化趋势预测存在健康指标构建依赖先验知识，预测精度低等问题，提出了基于卷积自编码器（convolutional auto-encodes，CAE）和融合注意力机制的门控循环单元（attention gated recurrent unit，AGRU）的滚动轴承退化趋势预测方法。首先，该方法通过快速傅里叶变换（fast fourier transform，FFT）将滚动轴承时域信号转换为频域信号，卷积自编码器从频域信号中自适应提取特征，编码特征通过评估选择构建健康指标（health indicators，HI），在此基础上，将健康指标输入融入注意力的门控循环单元网络(gate recurrent unit,GRU)模型，剪枝算法对模型参数进行优化，完成了滚动轴承性能退化趋势预测。结果表明，所提的方法能获得更准确的滚动轴承退化趋势预测。

Abstract

Aiming at problems of health indictor construction depending on prior knowledge and prediction accuracy being low for rolling bearing performance degradation trend prediction method in rotating machinery, a prediction method for rolling bearing degradation trend based on convolutional auto-encodes(CAE) and attention gated recurrent unit(AGRU) was proposed.Firstly, the method converted the rolling bearing time domain signal into frequency domain signal with fast fourier transform(FFT), and the features were extracted adaptively from frequency domain signal with convolutional auto-encodes.Then, the health indicators werw constructed from encoding features with evaluating and delsting. Finally the health indicators were input into the attention gated recurrent unit mode, and the pruning algorithm optimized the parameters to predict the performance degradation trend of rolling bearings. Results showed that the proposed method can obtain more accurate prediction results for rolling bearing performance degradation trend.

导出引用

焦玲玲1，陈捷1,2，刘连华1. 基于CAE和AGRU的滚动轴承退化趋势预测[J]. 振动与冲击, 2023, 42(12): 109-117

JIAO Lingling1，CHEN Jie1,2，LIU Lianhua1. Degradation trend prediction of rolling bearings based on CAE and AGRU[J]. Journal of Vibration and Shock, 2023, 42(12): 109-117

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