基于多传感器两级特征融合的滚动轴承故障诊断方法

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (8) : 199-207.

论文

刘仓，童靳于，包家汉，郑近德，潘海洋

作者信息 +

A rolling bearing fault diagnosis method based on multi-sensor two-stage feature fusion

LIU Cang,TONG Jinyu,BAO Jiahan,ZHENG Jinde,PAN Haiyang

Author information +

文章历史 +

摘要

针对单个传感器获取信息有限导致诊断精度不足的问题，提出一种基于多传感器两级特征融合的故障诊断方法，并将其应用于不同工况下的滚动轴承故障诊断中。首先，在第一阶段特征融合中，通过变分模态分解计算每个传感器振动信号的本征模态函数(Intrinsic Mode Function，IMF)，消除噪声等冗余信息；再根据IMF提取时域、频域和多尺度熵特征，在一维特征层面融合成一个多域特征集。其次，在第二阶段特征融合中，首先构建基于Swish激活函数和log(cosh)损失函数改进的深度自编码网络，在此基础上进一步融合多域特征集并进行故障分类。将所提模型应用于不同工况下的滚动轴承故障诊断数据集，试验结果表明，与现有方法相比，所提方法具有更高的分类准确率和鲁棒性。

Abstract

To address the problem of insufficient diagnostic accuracy due to the limited information obtained by a single sensor, a two-stage feature fusion fault diagnosis method based on multi-sensor is proposed and applied to the fault diagnosis of rolling bearing under different working conditions. First, the intrinsic mode function (IMF) of each sensor vibration signal is calculated by variational mode decomposition to eliminate redundant information such as noise. Then the time domain, frequency domain and multi-scale entropy features are extracted according to the IMF and fused into a multi-dimensional feature set at the one-dimensional feature level, which is the first stage of feature fusion. Second, an improved deep auto-encoder network based on the Swish activation function and log(cosh) loss function is firstly constructed, then the multi-domain feature set is further fused and fault classification is performed based on the improved deep auto-encoder network, which is the second stage of feature fusion. The proposed model is applied to the rolling bearing fault diagnosis data set under different working conditions. The experimental results show that the proposed method has higher classification accuracy and robustness compared with the existing methods.

导出引用

刘仓，童靳于，包家汉，郑近德，潘海洋. 基于多传感器两级特征融合的滚动轴承故障诊断方法[J]. 振动与冲击, 2022, 41(8): 199-207

LIU Cang,TONG Jinyu,BAO Jiahan,ZHENG Jinde,PAN Haiyang. A rolling bearing fault diagnosis method based on multi-sensor two-stage feature fusion[J]. Journal of Vibration and Shock, 2022, 41(8): 199-207

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