电子测量与仪器学报

赵志宏,李乐豪,李晴.一种轴承故障诊断的多任务深度学习方法[J].电子测量与仪器学报,2021,35(12):108-115

一种轴承故障诊断的多任务深度学习方法

Multi-task deep learning method for bearing fault diagnosis

DOI：

英文关键词:bearing multi-task deep learning convolutional neural network gated recurrent unit fault diagnosis

基金项目:国家自然科学基金（11972236,11790282）项目资助

作者	单位
赵志宏	1. 石家庄铁道大学省部共建交通工程结构力学行为与系统安全国家重点实验室，2. 石家庄铁道大学信息科学与技术学院
李乐豪	2. 石家庄铁道大学信息科学与技术学院
李晴	2. 石家庄铁道大学信息科学与技术学院

Author	Institution
Zhao Zhihong	1. State Key Laboratory of Mechanical Behavior in Traffic Engineering Structure and System Safety, Shijiazhuang Railway Institute,2. School of Computation and Informatics,Shijiazhuang Railway Institute
Li Lehao	2. School of Computation and Informatics, Shijiazhuang Railway Institute
Li Qing	2. School of Computation and Informatics, Shijiazhuang Railway Institute

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中文摘要:

提出一种基于多任务深度学习的故障诊断方法,将故障诊断任务分为故障分类和损伤程度识别。共享层采用卷积神经网络提取监测振动信号中蕴含的故障特征信息,两个子任务模块使用门控循环单元从共享层的输出中进一步提取特征,进行故障分类和损伤程度识别。在多任务深度学习方法中两个子任务模块可以通过共享层相互影响,提高模型的特征提取能力,获得更好的故障诊断性能。在轴承数据集上进行故障诊断实验,同时与故障分类单任务模型和损伤程度识别单任务模型进行对比, 以检验多任务深度学习方法的故障诊断性能,实验结果显示多任务深度学习模型在测试集上两个任务同时正确的准确率为 99. 79%。为进一步验证多任务深度学习方法的特征提取能力,在测试集中添加不同程度的高斯噪声进行故障诊断实验,在较强噪声情况下,多任务深度学习模型的准确率明显高于单任务深度学习模型。研究结果表明,多任务深度学习模型与单任务深度学习模型相比故障诊断准确率更高,同时抗噪性能更好,具有一定的实用价值。

英文摘要:

A fault diagnosis method based on multi-task deep learning is proposed, which classifies fault diagnosis tasks into fault classification and defect severity recognition. The shared layer uses convolution neural network to extract fault characteristic information contained in monitoring vibration signal, and the two subtask modules use gated recurrent unit to classify fault and recognize defect severity respectively. In multi-task deep learning method, the two subtask modules can interact with each other through the shared layers to promote the feature extraction ability of the model and make the whole model have better fault diagnosis performance. Fault diagnosis experiments are carried out on bearing dataset and compared with fault classification single-task model and defect severity identification single-task model to verify the fault diagnosis performance of multi-task deep learning method. The experimental results show that the accuracy of the multi-task deep learning model is 99. 79% for both tasks on the test set. In order to further verify the feature extraction capability of the multi-task deep learning method, different degrees of Gaussian noise were added to the test set for fault diagnosis experiment. Under the condition of strong noise, the accuracy of the multi-task deep learning model was significantly higher than that of the single-task deep learning model. The research results show that the multi-task deep learning model can diagnose fault more accurately and has better denoising than the single-task deep learning model, which has certain practical value.

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