计算机科学
生成对抗网络
学习迁移
断层(地质)
对抗制
原始数据
人工智能
试验数据
机器学习
生成语法
卷积神经网络
深度学习
数据挖掘
模式识别(心理学)
地震学
地质学
程序设计语言
作者
Zhiquan Cui,Yanlin Lu,Yan Xu,Shuya Cui
标识
DOI:10.1016/j.eswa.2024.123969
摘要
In order to solve the problem of compound fault diagnosis of diesel engine fuel injection system under the condition of few samples, a comprehensive diagnosis method based on generative adversarial networks and transfer learning based multi label classification models is proposed in this paper, which is based on convolutional attention networks. The generative adversarial networks are used to enhance the original data, and the enhanced data is used as the source data for transfer learning to ensure its effectiveness. Design multi-dimensional labels for compound fault diagnosis to enable the model to diagnose compound faults and their individual faults. Pre train the convolutional attention network with enhanced data generated from adversarial networks. Subsequently, the original data is used to integrate the feature information in the network and perform secondary training on the classification module. Finally, a compound fault diagnosis is implemented based on the model after secondary training. The effectiveness of the comprehensive diagnostic method proposed in the article is verified using real fault data of the fuel injection system. The diagnostic accuracy on test data reached 80%. The effectiveness of the comprehensive diagnostic method proposed in the article was verified using real fault data of the fuel injection system. The diagnostic accuracy on test data reached 80%. Compared to the method of directly training convolutional attention networks using raw data and the method of directly training convolutional attention networks without transfer learning using GAN enhanced datasets, the accuracy is improved by 16% and 8%, respectively.
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