A Novel Deep Multi-Source Domain Adaptation Framework for Bearing Fault Diagnosis Based on Feature-level and Task-specific Distribution 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.

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

Shock and Vibration ◽

10.1155/2020/8850976 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14 ◽

Cited By ~ 2

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.

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Simulation-driven Domain Adaptation for Rolling Element Bearing Fault Diagnosis

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2021.3103412 ◽

2021 ◽

pp. 1-1

Author(s):

Chenyu Liu ◽

Konstantinos Gryllias

Keyword(s):

Fault Diagnosis ◽

Domain Adaptation ◽

Rolling Element Bearing ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Rolling Element ◽

Element Bearing

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

Shock and Vibration ◽

10.1155/2021/6687331 ◽

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.

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Wasserstein distance based Asymmetric Adversarial Domain Adaptation in intelligent bearing fault diagnosis

Measurement Science and Technology ◽

10.1088/1361-6501/ac0a0c ◽

2021 ◽

Author(s):

Yu Ying ◽

Zhao Jun ◽

Tang Tang ◽

Wang Jingwei ◽

Chen Ming ◽

...

Keyword(s):

Fault Diagnosis ◽

Domain Adaptation ◽

Wasserstein Distance ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis

Sensors ◽

10.3390/s20010320 ◽

2020 ◽

Vol 20 (1) ◽

pp. 320 ◽

Cited By ~ 9

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.

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