基于人工数据融合的柴油机故障数据增强方法

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摘要针对基于数据驱动的柴油机故障诊断方法在训练数据匮乏时易过拟合、准确率低的问题，提出一种基于人工数据融合的数据增强方法，实现训练数据的增广。该方法将Wasserstein距离与梯度惩罚方法引入辅助分类生成对抗网络(auxiliary classifier generative adversarial network, ACGAN)，解决原始ACGAN训练不稳定的问题；将优化前后的ACGAN生成的两种人工数据按比例引入原始训练集中，从强化原有数据和优化诊断网络判定范围两个角度对训练集进行数据增强。经柴油机故障诊断实验验证，采用该方法对训练集进行数据增强后，在不同故障类型下的诊断准确率均有明显提高，且效果优于文中其他对比方法。

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	黄盟1
	毕晓阳2
	杨晓1
	李鑫1
	汤代杰1
	毕凤荣1

关键词 ：生成对抗网络, 数据融合, 柴油机, 数据增强

Abstract：Aiming at the problems of over-fitting and low accuracy of the data-driven diesel engine fault diagnosis method caused by the lack of data, a data augmentation method based on artificial data fusion was proposed to augment the training set. This method introduced the Wasserstein distance and gradient penalty into the auxiliary classifier generative adversarial network (ACGAN) in order to overcome the training instability of the original ACGAN. Two kinds of artificial data generated by the ACGANs before and after optimization were introduced into the original training set in proportion, and the training set was augmented from two perspectives: enhancement of the original data and optimization of the judgment range of diagnostic network. The analysis results of the measured vibration signals of the diesel engine show that the diagnosis accuracy under different fault types is improved by this data augmentation method, and the optimization effect is better than the other comparison method in this article.

Key words： generative adversarial network data fusion diesel engine data augmentation

收稿日期: 2022-06-08 出版日期: 2023-07-15

引用本文:

黄盟1，毕晓阳2，杨晓1，李鑫1，汤代杰1，毕凤荣1. 基于人工数据融合的柴油机故障数据增强方法[J]. 振动与冲击, 2023, 42(13): 278-286.
HUANG Meng1, BI Xiaoyang2, YANG Xiao1, LI Xin1, TANG Daijie1, BI Fengrong1. Diesel engine fault data augmentation method based on artificial data fusion. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(13): 278-286.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I13/278

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