Self-Supervised Joint Learning Fault Diagnosis Method Based on Three-Channel Vibration Images

机译：基于三通道振动图像的自我监督联合学习故障诊断方法

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摘要

The accuracy of bearing fault diagnosis is of great significance for the reliable operation of rotating machinery. In recent years, increasing attention has been paid to intelligent fault diagnosis techniques based on deep learning. However, most of these methods are based on supervised learning with a large amount of labeled data, which is a challenge for industrial applications. To reduce the dependence on labeled data, a self-supervised joint learning (SSJL) fault diagnosis method based on three-channel vibration images is proposed. The method combines self-supervised learning with supervised learning, makes full use of unlabeled data to learn fault features, and further improves the feature recognition rate by transforming the data into three-channel vibration images. The validity of the method was verified using two typical data sets from a motor bearing. Experimental results show that this method has higher diagnostic accuracy for small quantities of labeled data and is superior to the existing methods.

机译：轴承故障诊断的准确性对于旋转机械的可靠操作具有重要意义。近年来，基于深度学习的智能故障诊断技术，增加了越来越关注。然而，这些方法中的大多数是基于具有大量标记数据的监督学习，这是工业应用的挑战。为减少对标记数据的依赖性，提出了一种基于三通道振动图像的自我监督的联合学习（SSJL）故障诊断方法。该方法将自我监督的学习与监督学习结合起来，充分利用未标记的数据来学习故障特征，并通过将数据转换为三声道振动图像来进一步提高要素识别率。使用来自电动机轴承的两个典型数据组来验证该方法的有效性。实验结果表明，该方法具有较高的诊断准确性，少量标记数据，优于现有方法。

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期刊名称 Sensors (Basel Switzerland)
作者
Weiwei Zhang; Deji Chen; Yang Kong;
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年(卷),期 2021(21),14
年度 2021
页码 4774
总页数 23
原文格式 PDF
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机译：自我监督学习;故障诊断;三通振动图像;轴承;

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专利

1. The design of a new sparsogram for fast bearing fault diagnosis: Part 1 of the two related manuscripts that have a joint title as 'Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement - Parts 1 and 2' [J] . Peter W. Tse, Dong Wang Mechanical systems and signal processing . 2013,第2期

机译：用于快速轴承故障诊断的新稀疏图的设计：两个相关手稿的第1部分，它们的共同标题为“使用新颖稀疏度测量的两种基于振动的自动故障诊断方法-第1部分和第2部分”
2. Vibration Images-Driven Fault Diagnosis Based on CNN and Transfer Learning of Rolling Bearing under Strong Noise [J] . Hongwei Fan, Ceyi Xue, Xuhui Zhang, Shock and vibration . 2021,第a期

机译：基于CNN的振动图像驱动故障诊断与强噪声下滚动轴承的转移学习
3. An experimental methodology to evaluate machine learning methods for fault diagnosis based on vibration signals [J] . Rauber Thomas Walter, Loca Antonio Luiz da Silva, Boldt Francisco de Assis, Expert systems with applications . 2021,第Apra期

机译：基于振动信号评估机器学习方法的实验方法
4. Bearing Fault Diagnosis based on Convolutional Neural Network learning of time-domain vibration signal imaging [C] . Liuhao Ma, Jian Xu, Qiang Yang, Chinese Control and Decision Conference . 2019

机译：基于卷积神经网络的时域振动信号成像轴承故障诊断
5. Fault detection and diagnosis process for cracked rotor vibration systems using model-based approach. [D] . Boonyaprapasorn, Arsit. 2009

机译：裂纹转子振动系统的故障检测和诊断过程采用基于模型的方法。
6. A New Fault Diagnosis Method for a Diesel Engine Based on an Optimized Vibration Mel Frequency under Multiple Operation Conditions [O] . Haipeng Zhao, Jinjie Zhang, Zhinong Jiang, 2019

机译：基于最优振动梅尔频率的多工况柴油机故障诊断新方法
7. Bearing Fault Diagnosis under Variable Rotational Speeds Using Stockwell Transform-Based Vibration Imaging and Transfer Learning [O] . Md Junayed Hasan, Jong-Myon Kim 2018

机译：基于托斯变换的振动成像和转移学习，可变旋转速度下的轴承故障诊断

Self-Supervised Joint Learning Fault Diagnosis Method Based on Three-Channel Vibration Images

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