基于深度迁移学习的柴油机故障诊断研究

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (21) : 219-226.

论文

基于深度迁移学习的柴油机故障诊断研究

宋业栋1，马光伟1，裴国斌2，张俊红2,3

作者信息 +

Diesel engine fault diagnosis based on deep transfer learning

SONG Yedong1,MA Guangwei1,PEI Guobin2,ZHANG Junhong2,3

Author information +

文章历史 +

摘要

得益于大数据和人工智能的高速发展，数据驱动的智能故障诊断方法受到广泛关注。然而，在柴油机故障数据稀缺的情况下，传统神经网络训练容易出现过拟合且网络泛化能力差。为解决上述问题，提出一种基于深度迁移学习的小样本故障诊断方法。构建一种适用于柴油机原始振动信号的宽卷积核卷积长短期记忆神经网络，来提高故障数据特征提取和抗噪的能力，另外从原始数据自动提取特征，增强特征学习的智能性。进一步采用迁移学习方案，将大型标签源域数据的诊断知识迁移到目标域网络上，改进网络在目标域任务小样本条件下的学习和分类能力。在跨故障域和跨设备域迁移任务上进行算法评估，并与传统深度神经网络进行比较，验证了所提方法可有效改进小样本诊断性能。

Abstract

Data-driven intelligent fault diagnosis has lately attracted extensive interest, which deeply relates to the rapid development of big data and deep learning. However, when available fault data is limited, deep learning training is prone to overfitting, and the generalization ability of the trained network is affected. In this paper, an intelligent fault diagnosis method based on deep transfer learning is proposed to solve the problem of small samples. Construct a wide convolution kernel convolutional long short-term memory neural network to improve the feature extraction ability of diesel engine low signal-to-noise ratio fault data, and automatically extract features from the original data to enhance the intelligence of feature learning. A transfer learning scheme is further adopted to transfer the diagnostic knowledge of large-scale labeled source domain data to the target domain network, and improve the learning and classification capabilities of the network in the target domain tasks with small samples. Algorithm evaluation on cross-fault domain and cross- equipment domain, and comparison with traditional deep neural networks, verifies that the proposed transfer learning based on WKCL can effectively improve the performance of small sample diagnosis.

导出引用

宋业栋1，马光伟1，裴国斌2，张俊红2,3. 基于深度迁移学习的柴油机故障诊断研究[J]. 振动与冲击, 2023, 42(21): 219-226

SONG Yedong1,MA Guangwei1,PEI Guobin2,ZHANG Junhong2,3. Diesel engine fault diagnosis based on deep transfer learning[J]. Journal of Vibration and Shock, 2023, 42(21): 219-226

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