基于多维深度特征融合与改进麻雀搜索卷积神经网络的滚动轴承故障声发射诊断

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摘要针对滚动轴承早期、复合故障难以准确诊断与智能诊断模型超参数确定严重依赖专家先验知识问题，提出一种基于多维深度特征融合(MDFF)与改进麻雀搜索算法(ISSA)的滚动轴承故障声发射诊断方法。首先，用一维卷积与线性瓶颈反向残差二维卷积神经网络构建多输入卷积神经网络（CNN）结构的诊断模型，模型输入为滚动轴承声发射信号及其小波时频图，提出基于布伦纳梯度和信噪比的质量指标，在108种小波基中筛选出最佳时频图以提升输入数据质量。接着，采用特征金字塔网络将模型的一、二维低层与高层特征融合，建立深度融合的诊断模型。然后，将交叉混沌映射、自适应权重及融合的随机游走策略引入麻雀搜索算法中，以自适应获取MDFFCNN最优超参数。试验表明，对比近期多个主流智能诊断算法，所提方法可避免人工选择诊断模型超参数，对滚动轴承早期尤其复合故障具有更高的诊断精度和稳定性，模型诊断过程的智能化水平得到了进一步提高。

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	魏巍1
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	王之海1
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	柳小勤1
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	冯正江1
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	李佳慧1
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关键词 ：滚动轴承, 声发射, 深度学习, 改进麻雀搜索, 多维特征融合, 最佳时频图

Abstract：Aiming at the problem that the early and complex faults of rolling bearings are difficult to accurately diagnose and the determination of hyperparameters of the intelligent diagnosis model depends heavily on the prior knowledge of experts, an acoustic emission diagnosis method for rolling bearing faults based on Multidimensional Deep Feature Fusion (MDFF) and Improved Sparrow Search Algorithm (ISSA) is proposed. First, a multi-input convolutional neural network (CNN) diagnosis model is constructed using one-dimensional convolution and linear bottleneck inverse residual two-dimensional convolutional neural network. The input of the model is the rolling bearing acoustic emission signal and its wavelet time-frequency diagram. A quality index based on Brenner gradient and signal-to-noise ratio is proposed, and the best time-frequency image is selected from 108 wavelet bases to improve the quality of input data. Then, the feature pyramid network is used to fuse the one- and two-dimensional low-level and high-level features of the model to establish a deep fusion diagnostic model. Then, the random walk strategy of cross chaos mapping and adaptive weighting and fusion is introduced into the sparrow search algorithm to adaptively obtain the optimal hyperparameters of MDFFCNN. Experiments have shown that, compared with multiple mainstream intelligent diagnosis algorithms in the recent past, the proposed method can avoid manual selection of hyperparameters of the diagnosis model, and has higher diagnosis accuracy and stability for early-stage especially compound faults of rolling bearings, and the intelligent level of the model diagnosis process has been further improved.

Key words： Rolling bearing Acoustic emission Deep learning Improved sparrow search algorithm Multi- dimensional feature fusion Optimal time-frequency diagram

收稿日期: 2021-12-30 出版日期: 2023-04-15

引用本文:

魏巍1，2，王之海1，2，柳小勤1，2，冯正江1，2，李佳慧1，2. 基于多维深度特征融合与改进麻雀搜索卷积神经网络的滚动轴承故障声发射诊断[J]. 振动与冲击, 2023, 42(7): 65-76.
WEI Wei1,2, WANG Zhihai1,2, LIU Xiaoqin1,2, FENG Zhengjiang1,2, LI Jiahui1,2. AE Diagnosis of rolling bearing faults based on MDFF and ISSA. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(7): 65-76.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I7/65

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