基于RegNet-CSAM与ZOA-KELM模型的滚动轴承故障诊断

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (11) : 165-175.

论文

戚晓利，王兆俊，毛俊懿，王志文，崔德海，赵方祥

作者信息 +

Rolling bearing fault diagnosis based on RegNet-CSAM and ZOA-KELM model

QI Xiaoli, WANG Zhaojun, MAO Junyi, WANG Zhiwen, CUI Dehai, ZHAO Fangxiang

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文章历史 +

摘要

针对现有深度卷积神经网络对滚动轴承混合故障诊断效果不佳以及模型复杂度过高导致计算成本过大等问题，提出了一种基于RegNet-CSAM与ZOA-KELM模型的滚动轴承故障诊断方法。该模型由RegNet-CSAM网络和ZOA-KELM分类算法组成。首先，将融合了通道和空间特征的注意力机制CSAM与组卷积残差模块结合，提升该结构的表征能力，由此构建的RegNet-CSAM网络，模型复杂度为0.48GF；其次，在分类阶段将斑马优化核极限学习机（ZOA-KELM）替代原来网络中使用的Softmax函数完成最后的分类任务。滚动轴承故障诊断试验结果表明，RegNet网络对滚动轴承混合故障样本容易产生误判，CSAM的融入虽将RegNet网络的分类精度进一步提高，但是仍然存在一定程度的滚动轴承混合故障误判问题；而将ZOA-KELM替代Softmax函数后再对RegNet-CSAM网络输出特征进行分类，能够有效识别出滚动轴承的单一和混合故障，准确率达到了99.92%。所提方法对比其他网络，诊断精度最大提升5.02%，模型复杂度最大缩减32倍。

Abstract

To address the issues of poor diagnostic performance and high computational complexity in existing deep convolutional neural network models for rolling bearing fault diagnosis, a novel model based on RegNet-CSAM and ZOA-KELM is proposed. Firstly, the attention mechanism CSAM that combines channel and spatial features is combined with the group convolution residual module to improve the representation ability of the structure. The RegNet-CSAM network constructed thus has a model complexity of 0.48GF; Secondly, during the classification stage, the Zebra Optimization Algorithm-based Kernel Extreme Learning Machine (ZOA-KELM) is used to replace the original Softmax function in the network to accomplish the final classification task. Experimental results for rolling bearing fault diagnosis indicate that the RegNet network is prone to misjudge some samples with mixed faults. The integration of CSAM into the RegNet network improves the accuracy by 0.5%, but does not effectively address the problem of misjudgment for mixed faults. However, by employing ZOA-KELM to replace the Softmax function for feature classification in the RegNet-CSAM network, it can effectively identify both single and mixed rolling bearing faults, achieving an accuracy rate of 99.92%. Compared with other networks, the proposed method can improve the diagnosis accuracy by up to 5.02%, and reduce the model complexity by up to 32 times.

导出引用

戚晓利，王兆俊，毛俊懿，王志文，崔德海，赵方祥. 基于RegNet-CSAM与ZOA-KELM模型的滚动轴承故障诊断[J]. 振动与冲击, 2024, 43(11): 165-175

QI Xiaoli, WANG Zhaojun, MAO Junyi, WANG Zhiwen, CUI Dehai, ZHAO Fangxiang. Rolling bearing fault diagnosis based on RegNet-CSAM and ZOA-KELM model[J]. Journal of Vibration and Shock, 2024, 43(11): 165-175

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