Fault Diagnosis Using Unsupervised Transfer Learning Based on Adversarial Network

机译：基于对抗网络的无监督转移学习故障诊断

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摘要

The fault diagnosis is very important for the modern industry. Due to machine working conditions changing frequently, most of current fault diagnosis models built on the training (source) domain can't perform well in test (target) domain. In addition, in test domain, there are few labeled data to adjust model to be adaptive to test working conditions. Domain adaptation, as one type of transfer learning, can be used to solve this problem. This paper proposes a novel fault diagnosis method using unsupervised transfer learning based on adversarial network. In this method, deep neural network is used to extract feature of fault signal while the adversarial network is used to accomplish the transfer learning process. Firstly, the fault signal is converted into RGB images as inputs of networks. Then, the adversarial training methods are used, which includes three training processes: the regular training process using source data, the maximum discrepancy training process and the minimum discrepancy training process. These three steps are adversarial to each other to adjust the model to be more adaptive. The method is tested on motor bearing dataset provided by Case Western Reserve University (CWRU). The prediction accuracies are better than other four comparison methods.

机译：故障诊断对现代工业非常重要。由于机器工作条件的变化频繁，当前大多数基于训练（源）域的故障诊断模型在测试（目标）域中均无法很好地执行。另外，在测试领域，很少有标记数据可以调整模型以适应测试工作条件。领域适应作为一种转移学习，可以用来解决这个问题。提出了一种基于对抗网络的无监督转移学习的故障诊断新方法。该方法利用深度神经网络提取故障信号的特征，而利用对抗网络完成传递学习的过程。首先，将故障信号转换为RGB图像作为网络的输入。然后，使用对抗训练方法，该方法包括三个训练过程：使用源数据的常规训练过程，最大差异训练过程和最小差异训练过程。这三个步骤互为对立，以将模型调整为更具适应性。该方法在Case Western Reserve University（CWRU）提供的运动轴承数据集上进行了测试。预测精度优于其他四种比较方法。

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《IEEE International Conference on Automation Science and Engineering》|2019年|305-310|共6页
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Zhao Zhang; Xinyu Li; Long Wen; Liang Gao; Yiping Gao;
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Fault diagnosis; Generators; Feature extraction; Training; Data models; Employee welfare; Adaptation models;

机译：故障诊断;发电机;特征提取;培训;数据模型;员工福利;适应模型;

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1. Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks [J] . Liu Han, Zhou Jianzhong, Xu Yanhe, Neurocomputing . 2018,第NOVa13期

机译：基于生成对抗网络的深度神经网络的滚动轴承无监督故障诊断
2. A Deep Transfer Learning Method for Bearing Fault Diagnosis Based on Domain Separation and Adversarial Learning [J] . Shoubing Xiang, Jiangquan Zhang, Hongli Gao, Shock and vibration . 2021,第a期

机译：基于域分离和对抗性学习的轴承故障诊断深度传递学习方法
3. An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition [J] . Wang Zirui, Wang Jun, Wang Youren Neurocomputing . 2018,第OCTa8期

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4. Fault Diagnosis Using Unsupervised Transfer Learning Based on Adversarial Network [C] . Zhao Zhang, Xinyu Li, Long Wen, IEEE International Conference on Automation Science and Engineering . 2019

机译：基于对抗网络的无监督转移学习故障诊断
5. Deep Learning-Based Fault Diagnosis in Transmission Lines Via Long Short Term Memory Networks [D] . Tak, Ashok. 2021

机译：通过长短短期内存网络传输线的深度学习基础故障诊断
6. Generative Adversarial Phonology: Modeling Unsupervised Phonetic and Phonological Learning With Neural Networks [O] . Gašper Beguš 2020

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7. A Novel Deep Learning Model for Mechanical Rotating Parts Fault Diagnosis Based on Optimal Transport and Generative Adversarial Networks [O] . Xuanquan Wang, Xiongjun Liu, Ping Song, 2021

机译：基于最优运输和生成对抗网络的机械旋转零件故障诊断新的深层学习模型

Fault Diagnosis Using Unsupervised Transfer Learning Based on Adversarial Network

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