基于ASFF-AAKR和CNN-BILSTM滚动轴承寿命预测
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山东科技大学 机械电子工程学院

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TH133.33;TP183

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进气畸变条件下压入式矿用对旋主通风机失速起始扰动及其发展与传播机理


Life Prediction Based on ASFF-AAKR and CNN-BILSTM Rolling Bearings
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College of Energy and Mining Engineering Shandong University of Science and Technology

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    摘要:

    针对滚动轴承寿命预测精度低,构建健康指标困难的问题。提出了一种基于自适应特征融合(adaptively spatial feature fusion,ASFF)和自联想核回归模型(auto associative kernel regression, AAKR)与卷积神经网络(convolutional neural networks, CNN)和双向长短期记忆网络(bi-directional long-short term memory,BILSTM)的轴承剩余寿命预测模型。首先,在时域、频域和时频域提取多维特征,利用单调性和趋势性筛选敏感特征;其次利用ASFF-AAKR对敏感特征进行特征融合构建健康指标;最后,将健康指标输入到CNN和BILSTM中,实现对滚动轴承的寿命预测。结果表明:所构建的寿命预测模型优于其它模型,该方法具有更低的误差、寿命预测精度更高。

    Abstract:

    To address the issues of low accuracy in rolling bearing life prediction and the difficulty of constructing health indicators, a bearing remaining life prediction model based on adaptively spatial feature fusion (ASFF) and auto associative kernel regression (AAKR) combined with convolutional neural networks (CNN) and bi-directional long-short term memory networks (BILSTM) is proposed. Firstly, the multidimensional features are extracted in the time domain, frequency domain, and time-frequency domain, and the sensitive features are screened using monotonicity and trend; secondly, the sensitive features are feature fused using ASFF-AAKR to construct the health indicators; finally, the health indicators are inputted into CNN and BILSTM to realize the life prediction of rolling bearings. The results show that the constructed life prediction model is better than other models, and the method has lower error and higher life prediction accuracy.

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张永超,刘嵩寿,陈昱锡,等. 基于ASFF-AAKR和CNN-BILSTM滚动轴承寿命预测[J]. 科学技术与工程, , ():

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  • 收稿日期:2024-04-07
  • 最后修改日期:2024-04-30
  • 录用日期:2024-05-22
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