基于MADCNN的故障诊断方法研究  

Research on fault diagnosis method based on MADCNN

作　　者：王鸽 吴国新[1] 刘秀丽[1] Wang Ge;Wu Guoxin;Liu Xiuli(Key Laboratory of Modern Measurement and Control Technology,Ministry of Education,Beijing Information Science and Technology University,Beijing 100192,China)

机构地区：[1]北京信息科技大学现代测控技术教育部重点实验室,北京100192

出　　处：《电子测量与仪器学报》2023年第3期187-193,共7页Journal of Electronic Measurement and Instrumentation

基　　金：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。

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。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.

关 键 词：MADCNN 旋转机械 注意力机制 故障诊断 

分 类 号：TH132.41[机械工程—机械制造及自动化]