| 王 鸽,吴国新,刘秀丽.基于 MADCNN 的故障诊断方法研究[J].电子测量与仪器学报,2023,37(3):187-193 |
| 基于 MADCNN 的故障诊断方法研究 |
| Research on fault diagnosis method based on MADCNN |
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| DOI： |
| 中文关键词: MADCNN 旋转机械 注意力机制 故障诊断 |
| 英文关键词:MADCNN rotating machinery attentional mechanism fault diagnosis |
| 基金项目:国家重点研发计划项目( 2020YFB1713203)、国家自然科学基金( 61973041)、北京信息科技大学勤信人才项目(QXTCPC202120)资助 |
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| 中文摘要: |
| 针对旋转机械零部件进行故障诊断的方法包括传统方法和深度学习,传统方法往往需要大量的专家经验,且诊断精度
欠佳,提出一种注意力机制改进多尺度深度卷积神经网络(multi-scale attention deep convolutional neural network, MADCNN)的故
障诊断方法。 MADCNN 方法提供 3 个卷积通道,每个通道差异化的核尺寸原理有效拓宽网络,实现了对原始时域数据的多尺
度特征提取。 同时, CBAM 对提取的特征进一步赋予权重,增强了模型对不同类型故障的区分度。 采用凯斯西储大学(Case
Western Reserve University, CWRU)轴承故障数据和行星齿轮箱实验台故障数据分别进行实验验证,与传统深度卷积模型相比,
验证集准确率提高 7. 76%。 实验结果表明,该方法的诊断精度高,泛化性能好。 |
| 英文摘要: |
| Fault diagnosis methods for rotating machine parts include traditional methods and deep learning, and the former often
requires a lot of expert experience and the diagnosis accuracy is poor. A multi-scale attention deep convolutional neural network
(MADCNN) is proposed to improve the fault diagnosis method. The MADCNN method provides three convolutional channels, and the
principle of differential kernel size of each channel effectively widens the network to achieve multi-scale feature extraction of the original
time-domain data. At the same time, CBAM further assigns weights to the extracted features to enhance the differentiation of the model
for different types of faults. The accuracy of the validation set was improved by 7. 76% compared with the traditional deep convolutional
model by using the bearing failure data from Case Western Reserve University (CWRU) and the planetary gearbox test bench failure
data. The experimental results show that the method has high diagnostic accuracy and good generalization performance. |
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