期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
基于Fine-tune与DDC的变工况数控设备部件故障诊断
1
作者 王渤 杨越 +3 位作者 陆剑峰 余涛 颜鼎峰 徐煜昊 《机床与液压》 2024年第22期22-29,共8页
针对复杂工业环境下的数控设备部件故障诊断数据样本少、变工况诊断困难和准确率不高等问题,提出一种基于模型迁移的故障诊断方法。利用连续小波变换对不同工况下的原始振动数据进行预处理,建立二维时频数据集,并分为源域与目标域;利用... 针对复杂工业环境下的数控设备部件故障诊断数据样本少、变工况诊断困难和准确率不高等问题,提出一种基于模型迁移的故障诊断方法。利用连续小波变换对不同工况下的原始振动数据进行预处理,建立二维时频数据集,并分为源域与目标域;利用源域数据集与CNN进行模型预训练;分别引入微调(Fine-tune)与深度域混淆(DDC)2种迁移学习方式改进模型;最终实现了基于Fine-tune与基于DDC的故障诊断模型的构建。以轴承与数控铣刀2种部件为例进行实验验证,结果证明:Fine-tune与DDC均可以有效提高数控设备部件的故障诊断准确率,其中Fine-tune的泛化能力强,而DDC训练耗时更短且在复杂环境下的性能更优。 展开更多
关键词 故障诊断 变工况 卷积神经网络 Fine-tune 深度域混淆(ddc)
下载PDF
Regression model for civil aero-engine gas path parameter deviation based on deep domainadaptation with Res-BP neural network 被引量:10
2
作者 Xingjie ZHOU Xuyun FU +1 位作者 Minghang ZHAO Shisheng ZHONG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2021年第1期79-90,共12页
The variations in gas path parameter deviations can fully reflect the healthy state of aeroengine gas path components and units;therefore,airlines usually take them as key parameters for monitoring the aero-engine gas... The variations in gas path parameter deviations can fully reflect the healthy state of aeroengine gas path components and units;therefore,airlines usually take them as key parameters for monitoring the aero-engine gas path performance state and conducting fault diagnosis.In the past,the airlines could not obtain deviations autonomously.At present,a data-driven method based on an aero-engine dataset with a large sample size can be utilized to obtain the deviations.However,it is still difficult to utilize aero-engine datasets with small sample sizes to establish regression models for deviations based on deep neural networks.To obtain monitoring autonomy of each aero-engine model,it is crucial to transfer and reuse the relevant knowledge of deviation modelling learned from different aero-engine models.This paper adopts the Residual-Back Propagation Neural Network(Res-BPNN)to deeply extract high-level features and stacks multi-layer Multi-Kernel Maximum Mean Discrepancy(MK-MMD)adaptation layers to map the extracted high-level features to the Reproduce Kernel Hilbert Space(RKHS)for discrepancy measurement.To further reduce the distribution discrepancy of each aero-engine model,the method of maximizing domain-confusion loss based on an adversarial mechanism is introduced to make the features learned from different domains as close as possible,and then the learned features can be confused.Through the above methods,domain-invariant features can be extracted,and the optimal adaptation effect can be achieved.Finally,the effectiveness of the proposed method is verified by using cruise data from different civil aero-engine models and compared with other transfer learning algorithms. 展开更多
关键词 Civil aero-engine deep domain adaptation domain confusion Neural networks Transfer learning
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部