scholarly journals Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Jing An ◽  
Ping Ai ◽  
Dakun Liu
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Feature Learning ◽  
Target Domain ◽  
Source Domain ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Discriminative Feature ◽  
Joint Training ◽  
Domain Alignment

Deep learning techniques have been widely used to achieve promising results for fault diagnosis. In many real-world fault diagnosis applications, labeled training data (source domain) and unlabeled test data (target domain) have different distributions due to the frequent changes of working conditions, leading to performance degradation. This study proposes an end-to-end unsupervised domain adaptation bearing fault diagnosis model that combines domain alignment and discriminative feature learning on the basis of a 1D convolutional neural network. Joint training with classification loss, center-based discriminative loss, and correlation alignment loss between the two domains can adapt learned representations in the source domain for application to the target domain. Such joint training can also guarantee domain-invariant features with good intraclass compactness and interclass separability. Meanwhile, the extracted features can efficiently improve the cross-domain testing performance. Experimental results on the Case Western Reserve University bearing datasets confirm the superiority of the proposed method over many existing methods.

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 Deep Transfer Learning Method Based on 1D-CNN for Bearing Fault Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jun He ◽  
Xiang Li ◽  
Yong Chen ◽  
Danfeng Chen ◽  
Jing Guo ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Learning Method ◽  
Target Domain ◽  
Source Domain ◽  
Second Order Statistics ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

In mechanical fault diagnosis, it is impossible to collect massive labeled samples with the same distribution in real industry. Transfer learning, a promising method, is usually used to address the critical problem. However, as the number of samples increases, the interdomain distribution discrepancy measurement of the existing method has a higher computational complexity, which may make the generalization ability of the method worse. To solve the problem, we propose a deep transfer learning method based on 1D-CNN for rolling bearing fault diagnosis. First, 1-dimension convolutional neural network (1D-CNN), as the basic framework, is used to extract features from vibration signal. The CORrelation ALignment (CORAL) is employed to minimize marginal distribution discrepancy between the source domain and target domain. Then, the cross-entropy loss function and Adam optimizer are used to minimize the classification errors and the second-order statistics of feature distance between the source domain and target domain, respectively. Finally, based on the bearing datasets of Case Western Reserve University and Jiangnan University, seven transfer fault diagnosis comparison experiments are carried out. The results show that our method has better performance.

scholarly journals Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 320 ◽  
Author(s):  
Xiaodong Wang ◽  
Feng Liu
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Training Dataset ◽  
Great Success ◽  
Target Domain ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Class Level ◽  
Triplet Loss ◽  
Data Level

Recently, deep learning methods are becomingincreasingly popular in the field of fault diagnosis and achieve great success. However, since the rotation speeds and load conditions of rotating machines are subject to change during operations, the distribution of labeled training dataset for intelligent fault diagnosis model is different from the distribution of unlabeled testing dataset, where domain shift occurs. The performance of the fault diagnosis may significantly degrade due to this domain shift problem. Unsupervised domain adaptation has been proposed to alleviate this problem by aligning the distribution between labeled source domain and unlabeled target domain. In this paper, we propose triplet loss guided adversarial domain adaptation method (TLADA) for bearing fault diagnosis by jointly aligning the data-level and class-level distribution. Data-level alignment is achieved using Wasserstein distance-based adversarial approach, and the discrepancy of distributions in feature space is further minimized at class level by the triplet loss. Unlike other center loss-based class-level alignment approaches, which hasto compute the class centers for each class and minimize the distance of same class center from different domain, the proposed TLADA method concatenates 2 mini-batches from source and target domain into a single mini-batch and imposes triplet loss to the whole mini-batch ignoring the domains. Therefore, the overhead of updating the class center is eliminated. The effectiveness of the proposed method is validated on CWRU dataset and Paderborn dataset through extensive transfer fault diagnosis experiments.

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 Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation

2019 ◽  
Vol 33 ◽  
pp. 3296-3303 ◽  
Author(s):  
Chao Chen ◽  
Zhihong Chen ◽  
Boyuan Jiang ◽  
Xinyu Jin
Keyword(s):  
Domain Adaptation ◽  
Feature Learning ◽  
Feature Space ◽  
Feature Representation ◽  
Learning Method ◽  
Target Domain ◽  
Shared Feature ◽  
Class Separability ◽  
Discriminative Feature ◽  
Domain Alignment

Recently, considerable effort has been devoted to deep domain adaptation in computer vision and machine learning communities. However, most of existing work only concentrates on learning shared feature representation by minimizing the distribution discrepancy across different domains. Due to the fact that all the domain alignment approaches can only reduce, but not remove the domain shift, target domain samples distributed near the edge of the clusters, or far from their corresponding class centers are easily to be misclassified by the hyperplane learned from the source domain. To alleviate this issue, we propose to joint domain alignment and discriminative feature learning, which could benefit both domain alignment and final classification. Specifically, an instance-based discriminative feature learning method and a center-based discriminative feature learning method are proposed, both of which guarantee the domain invariant features with better intra-class compactness and inter-class separability. Extensive experiments show that learning the discriminative features in the shared feature space can significantly boost the performance of deep domain adaptation methods.

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