基于CFasterVit-TFAM与COS-UMAP模型的滚动轴承故障诊断

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振动与冲击 ›› 2025, Vol. 44 ›› Issue (10) : 287-300.

故障诊断分析

基于CFasterVit-TFAM与COS-UMAP模型的滚动轴承故障诊断

戚晓利，崔德海*，王志文，赵方祥，王兆俊，毛俊懿，杨文好

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Fault diagnosis of rolling bearings based on CFasterVit-TFAM and COS-UMAP model

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

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摘要

针对FasterVit网络存在的注意力机制失衡、池化策略缺陷导致部分重要特征无法保留和损失函数不能全面考虑所有类别的信息导致学习到的特征比较分散等问题，提出了一种基于CFasterVit-TFAM与COS-UMAP模型的滚动轴承故障诊断方法。该模型由FasterVit-TFAM网络、COS-UMAP降维算法和激活函数CMSD-Softmax组成。首先，提出了一种新的注意力机制TFAM，并与FasterVit网络结合，提升了FasterVit网络信息关注的均衡性和表征能力；其次，将基于余弦均匀流形逼近与投影（COS-UMAP）降维算法取代FasterVit网络全连接层前最后一次池化操作，有效筛选并保留多维数据中的重要特征；最后，将类距均值标准差损失函数替换Softmax激活函数中的交叉熵损失函数，更全面的学习特征并提高模型的泛化性。XJTU滚动轴承故障实验结果表明，TFAM注意力机制和其他注意力机制相比诊断准确率最大提升8%，COS-UMAP对比其他降维算法诊断准确率最大提升15.8%，CMSD对比交叉熵损失函数诊断准确率提升0.5%，所提模型对故障样本的识别准确率达到了99.6%，相比FasterVit提升了1.4%，相较于其他网络模型最大提升7.8%；东南大学滚动轴承数据集仿真验证实验结果表明，所提模型对故障样本识别率达98.6%，相比FasterVit提升了2.2%，平均每轮训练时间缩短了16.92s，对比其他网络模型最大提升12.2%，有效提高了滚动轴承故障诊断模型的准确率和泛化性能。

Abstract

Given the issues of imbalanced attention mechanisms, conservative pooling strategies, and the loss function's inability to comprehensively consider information from all classes leads to the learned features being relatively scattered in the FasterVit network, a rolling bearing fault diagnosis method based on the CFasterVit-TFAM and COS-UMAP models is proposed. The model consists of the FasterVit-TFAM network, the COS-UMAP dimensionality reduction algorithm, and the activation function CMSD-Softmax. Firstly, a new attention mechanism TFAM is proposed and combined with the FasterVit network to improve the balance and representation ability of information attention in the FasterVit network. Secondly, the COS-UMAP dimensionality reduction algorithm is used to replace the last pooling operation before the fully connected layer of the FasterVit network, effectively filtering and retaining important features in multidimensional data. Finally, replacing the cross-entropy loss function in the Softmax activation function with the mean standard deviation loss function allows for a more comprehensive learning of features and improves the model's generalization. The XJTU rolling bearing mixed fault experiment results show that the diagnostic accuracy of the TFAM attention mechanism is increased by 8% compared to other attention mechanisms, and the diagnostic accuracy of the COS-UMAP is increased by 15.8% compared to other dimensionality reduction algorithms. The diagnostic accuracy of the CMSD is increased by 0.5% compared to the cross entropy loss function. The proposed model achieves a recognition accuracy of 99.6% for fault samples, which is 1.4% higher than that of FasterVit and 7.8% higher than that of other network models. The simulation results of the rolling bearing dataset from Southeast University show that the proposed model achieves a recognition rate of 98.6% for fault samples, which is 2.2% higher than that of FasterVit. The average training time per round is reduced by 16.92 seconds, which is a maximum improvement of 12.2% compared to other network models, effectively improving the accuracy and generalization performance of the rolling bearing fault diagnosis model.

导出引用

戚晓利, 崔德海, 王志文, 赵方祥, 王兆俊, 毛俊懿, 杨文好. 基于CFasterVit-TFAM与COS-UMAP模型的滚动轴承故障诊断[J]. 振动与冲击, 2025, 44(10): 287-300

QI Xiaoli, CUI Dehai, WANG Zhiwen, ZHAO Fangxiang, WANG Zhaojun, MAO Junyi, YANG Wenhao. Fault diagnosis of rolling bearings based on CFasterVit-TFAM and COS-UMAP model[J]. Journal of Vibration and Shock, 2025, 44(10): 287-300

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