scholarly journals Multi-Scale Capsule Attention Network and Joint Distributed Optimal Transport for Bearing Fault Diagnosis under Different Working Loads

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6696
Author(s):  
Zihao Sun ◽  
Xianfeng Yuan ◽  
Xu Fu ◽  
Fengyu Zhou ◽  
Chengjin Zhang
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Optimal Transport ◽  
Domain Adaptation ◽  
Data Distribution ◽  
Attention Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Diagnosis Accuracy

In recent years, intelligent fault diagnosis methods based on deep learning have developed rapidly. However, most of the existing work performs well under the assumption that training and testing samples are collected from the same distribution, and the performance drops sharply when the data distribution changes. For rolling bearings, the data distribution will change when the load and speed change. In this article, to improve fault diagnosis accuracy and anti-noise ability under different working loads, a transfer learning method based on multi-scale capsule attention network and joint distributed optimal transport (MSCAN-JDOT) is proposed for bearing fault diagnosis under different loads. Because multi-scale capsule attention networks can improve feature expression ability and anti-noise performance, the fault data can be better expressed. Using the domain adaptation ability of joint distribution optimal transport, the feature distribution of fault data under different loads is aligned, and domain-invariant features are learned. Through experiments that investigate bearings fault diagnosis under different loads, the effectiveness of MSCAN-JDOT is verified; the fault diagnosis accuracy is higher than that of other methods. In addition, fault diagnosis experiment is carried out in different noise environments to demonstrate MSCAN-JDOT, which achieves a better anti-noise ability than other transfer learning methods.

scholarly journals Rolling Bearing Incipient Fault Diagnosis Method Based on Improved Transfer Learning with Hybrid Feature Extraction

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7894
Author(s):  
Zhengni Yang ◽  
Rui Yang ◽  
Mengjie Huang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Data Distribution ◽  
Rolling Bearing ◽  
Data Driven ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Incipient Fault ◽  
Incipient Fault Diagnosis

Data-driven based rolling bearing fault diagnosis has been widely investigated in recent years. However, in real-world industry scenarios, the collected labeled samples are normally in a different data distribution. Moreover, the features of bearing fault in the early stages are extremely inconspicuous. Due to the above mentioned problems, it is difficult to diagnose the incipient fault under different scenarios by adopting the conventional data-driven methods. Therefore, in this paper a new unsupervised rolling bearing incipient fault diagnosis approach based on transfer learning is proposed, with a novel feature extraction method based on a statistical algorithm, wavelet scattering network, and a stacked auto-encoder network. Then, the geodesic flow kernel algorithm is adopted to align the feature vectors on the Grassmann manifold, and the k-nearest neighbor classifier is used for fault classification. The experiment is conducted based on two bearing datasets, the bearing fault dataset of Case Western Reserve University and the bearing fault dataset of Xi’an Jiaotong University. The experiment results illustrate the effectiveness of the proposed approach on solving the different data distribution and incipient bearing fault diagnosis issues.

scholarly journals One-Dimensional Multi-Scale Domain Adaptive Network for Bearing-Fault Diagnosis under Varying Working Conditions

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6039
Author(s):  
Kai Wang ◽  
Wei Zhao ◽  
Aidong Xu ◽  
Peng Zeng ◽  
Shunkun Yang
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Target Domain ◽  
One Dimensional ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Classifier Adaptation ◽  
Feature Adaptation

Data-driven bearing-fault diagnosis methods have become a research hotspot recently. These methods have to meet two premises: (1) the distributions of the data to be tested and the training data are the same; (2) there are a large number of high-quality labeled data. However, machines usually work under different working conditions in practice, which challenges these prerequisites due to the fact that the data distributions under different working conditions are different. In this paper, the one-dimensional Multi-Scale Domain Adaptive Network (1D-MSDAN) is proposed to address this issue. The 1D-MSDAN is a kind of deep transfer model, which uses both feature adaptation and classifier adaptation to guide the multi-scale convolutional neural network to perform bearing-fault diagnosis under varying working conditions. Feature adaptation is performed by both multi-scale feature adaptation and multi-level feature adaptation, which helps in finding domain-invariant features by minimizing the distribution discrepancy between different working conditions by using the Multi-kernel Maximum Mean Discrepancy (MK-MMD). Furthermore, classifier adaptation is performed by entropy minimization in the target domain to bridge the source classifier and target classifier to further eliminate domain discrepancy. The Case Western Reserve University (CWRU) bearing database is used to validate the proposed 1D-MSDAN. The experimental results show that the diagnostic accuracy for the 12 transfer tasks performed by 1D-MSDAN was superior to that of the mainstream transfer learning models for bearing-fault diagnosis under variable working conditions. In addition, the transfer learning performance of 1D-MSDAN for multi-target domain adaptation and real industrial scenarios was also verified.

scholarly journals Multi-scale deep intra-class transfer learning for bearing fault diagnosis

2020 ◽  
Vol 202 ◽  
pp. 107050 ◽  
Author(s):  
Xu Wang ◽  
Changqing Shen ◽  
Min Xia ◽  
Dong Wang ◽  
Jun Zhu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale
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