Adversarial domain adaptation convolutional neural network for intelligent recognition of bearing faults

Wu, You; Zhao, Rongzhen; Ma, Hongru; He, Qiang; Du, Sichun; Wu, Jie

doi:10.1016/j.measurement.2022.111150

Cited by 34 publications

(9 citation statements)

References 30 publications

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“…Xu et al [42] constructed a dual adversarial fault diagnosis method combining two classifiers and a domain discriminator, and reduced the distribution discrepancy between the two domains by computing the MMD. Wu et al [43] designed an adversarial domain adaptation convolutional neural network for cross-domain fault diagnosis using two classifiers and a domain discriminator, which introduce adversarial learning and MMD in the feature and prediction label spaces respectively for domain adaptation.…”

Section: Methods Of Combining Distance Metric Calculation and Adversa...mentioning

confidence: 99%

Category-aware dual adversarial domain adaptation model for rolling bearings fault diagnosis under variable conditions

Jiang

et al. 2023

Meas. Sci. Technol.

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The domain adaptation methods have good performance in solving the distribution discrepancy of vibration signals of rolling bearings under variable conditions, but without considering the alignment of different categories. To this end, a new dual adversarial domain adaptation (2ADA) mechanism for feature intra-category is proposed and a fault diagnosis model based on 2ADA is built in this paper. The method effectively uses category information to achieve category awareness, and avoids misclassification at the fuzzy decision boundary. In the training process, the multiple-kernel maximum mean discrepancy is used to reduce the discrepancy and perform a global alignment. The category-level alignment is performed when 2ADA is activated, which due to obtain more comprehensive domain adaptation performance and improve the accuracy of fault classification. The results of fault diagnosis experiments on the CWRU bearing dataset and the rotating machinery fault platform dataset demonstrate that, the diagnosis accuracy of the proposed method is improved by up to 15.46% and 5.75% on tasks with high domain shift when compared with CNN method, which verifies the effectiveness of the method.

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Section: Methods Of Combining Distance Metric Calculation and Adversa...mentioning

confidence: 99%

Category-aware dual adversarial domain adaptation model for rolling bearings fault diagnosis under variable conditions

Jiang

et al. 2023

Meas. Sci. Technol.

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“…These potential representations are then applied to the target domain data. For unsupervised domain adaptation, one of the most popular techniques is the domain adversarial neural network (DANN) [23]. The core idea of the domain adversarial adaptation method is to learn a good enough representation to reduce the difference in edge distribution between the source domain and the target domain at the representation level.…”

Section: Domain Adaptationmentioning

confidence: 99%

Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method

Pan,

Chen,

Zhao

et al. 2024

Applied Sciences

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Targeting the challenge of variable working conditions in bearing fault diagnosis, most of the fault diagnosis methods based on transfer learning focus on the transfer of knowledge, resulting in a poor diagnosis effect in the target domain. To solve the problem of transfer performance degradation, a multi-perception graph convolution transfer network (MPGCTN) is proposed. The MPGCTN is composed of a graph generation module, graph perception module, and domain discrimination module. In the graph generation module, a one-dimensional convolution neural network (1-D CNN) is used to extract features from the input, and then the structural features of samples are mined in the graph generation layer to construct the sample graph. In the following graph perception module, a multi-perception graph convolution network is designed to model the sample graph and learn the data structure information of the sample. Finally, in the domain discrimination module, the method is used to align the structural differences of the case graphs in different domains. Experimental results from experiments on Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets show that the proposed method is effective and superior.

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“…Using adversarial learning to reduce domain distribution difference is another important direction in fault diagnosis. Wu et al [29] used adversarial learning and MMD for domain adaptation of feature space and prediction labels to achieve bearing fault diagnosis. Li et al [30] developed a deep adversarial transfer learning network to learn domain invariant features from domain data, which improved the accuracy of fault detection for bearings and gearboxes.…”

Section: Introductionmentioning

confidence: 99%

Few-shot condition diagnosis of rolling bearing using adversarial transfer network with class aggregation-guided

Tian,

Zhen,

Sun

et al. 2024

Meas. Sci. Technol.

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For the challenge of fault identification under limited labeled data in engineering applications,a novel adversarial transfer network with class aggregation-guided (ATN-CA) is proposed for few-shot condition diagnosis of bearings. The ATN-CA can focus on the discrepancy features of bearings by the proposed local discrepancy feature representation, avoiding that the features extracted by a single neural network may omit important fault information. Moreover, the further proposed class aggregation-guided strategy uses the semantic information of signals to guide the dynamic adaptation of marginal and conditional distributions of source and target data, which shortens the distribution distance of the same category in different domains, thus completing the transfer diagnosis. By comparing with other current methods on the artificial and real bearing fault datasets,results show this proposed method has the highest test precision and the smallest accuracy deviation in bearing transfer diagnosis.

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Adversarial domain adaptation convolutional neural network for intelligent recognition of bearing faults

Cited by 34 publications

References 30 publications

Category-aware dual adversarial domain adaptation model for rolling bearings fault diagnosis under variable conditions

Category-aware dual adversarial domain adaptation model for rolling bearings fault diagnosis under variable conditions

Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method

Few-shot condition diagnosis of rolling bearing using adversarial transfer network with class aggregation-guided

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