| 张 焱,何姝钡,韩 延,黄庆卿.多小波系数增强动态聚合联邦深度网络的多工况故障诊断[J].电子测量与仪器学报,2023,37(5):68-78 |
| 多小波系数增强动态聚合联邦深度网络的多工况故障诊断 |
| Multi-wavelet coefficients enhanced dynamic aggregation federal deep network for fault diagnosis under multiple conditions |
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| DOI： |
| 中文关键词: 故障诊断 小样本 多工况 联邦学习 特征增强 |
| 英文关键词:fault diagnosis small sample multiple conditions federated learning feature enhancement |
| 基金项目:中国博士后科学基金(2022MD713687)、国家自然科学基金(51705056)、重庆市自然科学基金面上项目( cstc2021jcyj-msxmX0556)、重庆市教委科学技术研究项目(KJQN202100612)资助 |
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| Author | Institution |
| Zhang Yan | 1. Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications,2. Institute of Industrial Internet, Chongqing University of Posts and Telecommunications |
| He Shubei | 1. Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications,2. Institute of Industrial Internet, Chongqing University of Posts and Telecommunications |
| Han Yan | 1. Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications,2. Institute of Industrial Internet, Chongqing University of Posts and Telecommunications |
| Huang Qingqing | 1. Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications,2. Institute of Industrial Internet, Chongqing University of Posts and Telecommunications |
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| 中文摘要: |
| 针对分布式场景下单节点样本有限、多节点间工况分布不平衡等导致的深度学习故障诊断精度低的问题,提出一种多
小波系数增强动态聚合联邦深度网络用于分布式小样本下的多工况机械故障诊断。 提出多小波系数增强动态聚合联邦深度网
络的诊断框架,单终端节点从本地样本中提取小波系数特征,提出多小波系数深度网络融合的特征增强方法,局部模型从多样
性小波系数集合中提取更具判别性故障特征;聚合节点通过对多终端节点局部模型的聚合以构建全局联邦深度网络模型,并用
于多工况故障诊断;为降低多节点间数据非独立同分布的影响,提出平衡模型贡献度的联邦动态加权聚合算法。 轴承振动数据
分析结果表明,所提方法能在分布式小样本条件下实现高精度的多工况故障诊断。 |
| 英文摘要: |
| To address the low accuracy of deep learning based fault diagnosis under distributed scenarios that caused by limited sample of
single node and unbalanced distribution of working conditions of multiple nodes, et al, a multi-wavelet coefficients enhanced dynamic
aggregation federal deep network ( MWCE-FedDWA) is proposed for fault diagnosis under multiple conditions with distributed small
samples. A framework for fault diagnosis using MWCE-FedDWA is proposed, wavelet coefficient features are extracted by each terminal
node from its local samples, a method based on multi-wavelet coefficient fusion in deep network is proposed for feature enhancement,
each local model utilizes a set of diversified wavelet coefficients to extract more discriminative fault features. A global federal deep
network model is constructed in aggregation node by aggregating the local models from multiple terminal nodes, and then adopted for fault
diagnosis under multiple conditions. To reduce the influence of non-independent and identically distributed data among multiple nodes, a
federated dynamic weighted aggregation algorithm is proposed to balance the contribution of local models. The results on bearing vibration
data show that the proposed method can achieve high-precision diagnosis under multiple conditions with distributed small samples. |
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