Intelligent fault diagnosis of rotating machinery under variable working conditions based on deep transfer learning with fusion of local and global time–frequency features

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Xiao Yu, Songcheng Wang, Hongyang Xu, Kun Yu, Ke Feng, Yongchao Zhang, Xiaowen Liu
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引用次数: 0

Abstract

With the development of deep learning methods, the data-driven fault diagnosis methods have attracted a great deal of interest. However, as for the data-driven fault diagnosis methods, technology has to overcome various difficulties in the practical industrial scenarios, such as variable working conditions, insufficient effective samples, and environmental noise interference. Combining with the time–frequency analysis of vibration signals, a domain adaptation fault diagnosis model based on ResNet and Transformer (DAFDMRT) is proposed in this work, aiming to solve the problems encountered by current rotating machinery fault diagnosis methods in the field of application. Firstly, the vibration signal is processed by wavelet packet transform and the time–frequency information grayscale maps is constructed. Next, a deep fusion feature extraction network combining ResNet and Transformer encoder, is designed for the extraction and fusion of the local and global features of multi-scale time–frequency information. Finally, the multi-kernel maximum mean discrepancy is applied to measure and minimize the distribution difference between the deep features of source and target domain, thereby improving the diagnostic performance of the diagnosis model in variable working conditions. In this work, comparative experiments are conducted as for bearing and gearbox datasets under variable working conditions. The results indicate that DAFDMRT can show excellent performances in terms of fault diagnosis and generalization ability.
基于局部与全局时频特征融合的深度迁移学习的变工况旋转机械故障智能诊断
随着深度学习方法的发展,数据驱动的故障诊断方法引起了人们的广泛关注。然而,对于数据驱动的故障诊断方法,技术上还需要克服实际工业场景中的各种困难,如工况多变、有效样本不足、环境噪声干扰等。本文结合振动信号的时频分析,提出了一种基于ResNet和Transformer的域自适应故障诊断模型(DAFDMRT),旨在解决当前旋转机械故障诊断方法在应用领域中遇到的问题。首先对振动信号进行小波包变换,构造时频信息灰度图;其次,结合ResNet和Transformer编码器设计深度融合特征提取网络,对多尺度时频信息的局部特征和全局特征进行提取和融合。最后,利用多核最大均值差异来度量源域与目标域深度特征之间的分布差异并使之最小化,从而提高了诊断模型在变工况下的诊断性能。在这项工作中,对轴承和齿轮箱数据集进行了不同工况下的对比实验。结果表明,DAFDMRT在故障诊断和泛化能力方面表现出优异的性能。
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
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