scholarly journals Domain Adaptation for Intelligent Fault Diagnosis under Different Working Conditions

2020 ◽  
Vol 319 ◽  
pp. 03001
Author(s):  
Weigui Li ◽  
Zhuqing Yuan ◽  
Wenyu Sun ◽  
Yongpan Liu
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Domain Adaptation ◽  
Transfer Problem ◽  
Intelligent Manufacturing ◽  
Maximum Mean Discrepancy ◽  
Bearing Fault Diagnosis ◽  
Cross Domain ◽  
Conditional Maximum ◽  
Adaptation Method

Recently, deep learning algorithms have been widely into fault diagnosis in the intelligent manufacturing field. To tackle the transfer problem due to various working conditions and insufficient labeled samples, a conditional maximum mean discrepancy (CMMD) based domain adaptation method is proposed. Existing transfer approaches mainly focus on aligning the single representation distributions, which only contains partial feature information. Inspired by the Inception module, multi-representation domain adaptation is introduced to improve classification accuracy and generalization ability for cross-domain bearing fault diagnosis. And CMMD-based method is adopted to minimize the discrepancy between the source and the target. Finally, the unsupervised learning method with unlabeled target data can promote the practical application of the proposed algorithm. According to the experimental results on the standard dataset, the proposed method can effectively alleviate the domain shift problem.

scholarly journals A New Deep Convolutional Domain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Yongchao Zhang ◽  
Zhaohui Ren ◽  
Shihua Zhou
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Domain Adaptation ◽  
Classification Error ◽  
Mechanical Equipment ◽  
Target Domain ◽  
Source Domain ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Cross Domain

Effective fault diagnosis methods can ensure the safe and reliable operation of the machines. In recent years, deep learning technology has been applied to diagnose various mechanical equipment faults. However, in real industries, the data distribution under different working conditions is often different, which leads to serious degradation of diagnostic performance. In order to solve the issue, this study proposes a new deep convolutional domain adaptation network (DCDAN) method for bearing fault diagnosis. This method implements cross-domain fault diagnosis by using the labeled source domain data and the unlabeled target domain data as training data. In DCDAN, firstly, a convolutional neural network is applied to extract features of source domain data and target domain data. Then, the domain distribution discrepancy is reduced through minimizing probability distribution distance of multiple kernel maximum mean discrepancies (MK-MMD) and maximizing the domain recognition error of domain classifier. Finally, the source domain classification error is minimized. Extensive experiments on two rolling bearing datasets verify that the proposed method can implement accurate cross-domain fault diagnosis under different working conditions. The study may provide a promising tool for bearing fault diagnosis under different working conditions.

scholarly journals Multi-Layer domain adaptation method for rolling bearing fault diagnosis

2019 ◽  
Vol 157 ◽  
pp. 180-197 ◽  
Author(s):  
Xiang Li ◽  
Wei Zhang ◽  
Qian Ding ◽  
Jian-Qiao Sun
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Adaptation Method

scholarly journals Dynamic Distribution Adaptation Based Transfer Network for Cross Domain Bearing Fault Diagnosis

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Yixiao Liao ◽  
Ruyi Huang ◽  
Jipu Li ◽  
Zhuyun Chen ◽  
Weihua Li
Keyword(s):  
Fault Diagnosis ◽  
Conditional Distribution ◽  
Conditional Distributions ◽  
Target Domain ◽  
Maximum Mean Discrepancy ◽  
Source Domain ◽  
Dynamic Distribution ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Cross Domain

AbstractIn machinery fault diagnosis, labeled data are always difficult or even impossible to obtain. Transfer learning can leverage related fault diagnosis knowledge from fully labeled source domain to enhance the fault diagnosis performance in sparsely labeled or unlabeled target domain, which has been widely used for cross domain fault diagnosis. However, existing methods focus on either marginal distribution adaptation (MDA) or conditional distribution adaptation (CDA). In practice, marginal and conditional distributions discrepancies both have significant but different influences on the domain divergence. In this paper, a dynamic distribution adaptation based transfer network (DDATN) is proposed for cross domain bearing fault diagnosis. DDATN utilizes the proposed instance-weighted dynamic maximum mean discrepancy (IDMMD) for dynamic distribution adaptation (DDA), which can dynamically estimate the influences of marginal and conditional distribution and adapt target domain with source domain. The experimental evaluation on cross domain bearing fault diagnosis demonstrates that DDATN can outperformance the state-of-the-art cross domain fault diagnosis methods.

scholarly journals Bearing Fault Diagnosis Based on Multilayer Domain Adaptation

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Bingru Yang ◽  
Qi Li ◽  
Liang Chen ◽  
Changqing Shen ◽  
Sundararajan Natarajan
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Conditional Distributions ◽  
Training Set ◽  
Maximum Mean Discrepancy ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Invariant Features ◽  
Signal Processing Methods ◽  
Transfer Tasks

Bearing fault diagnosis plays a vitally important role in practical industrial scenarios. Deep learning-based fault diagnosis methods are usually performed on the hypothesis that the training set and test set obey the same probability distribution, which is hard to satisfy under the actual working conditions. This paper proposes a novel multilayer domain adaptation (MLDA) method, which can diagnose the compound fault and single fault of multiple sizes simultaneously. A special designed residual network for the fault diagnosis task is pretrained to extract domain-invariant features. The multikernel maximum mean discrepancy (MK-MMD) and pseudo-label learning are adopted in multiple layers to take both marginal distributions and conditional distributions into consideration. A total of 12 transfer tasks in the fault diagnosis problem are conducted to verify the performance of MLDA. Through the comparisons of different signal processing methods, different parameter settings, and different models, it is proved that the proposed MLDA model can effectively extract domain-invariant features and achieve satisfying results.

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