Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis

Xu, Kun; Li, Shunming; Li, Ranran; Lu, Jiming; Li, Xianglian; Zeng, Mengjie

doi:10.3390/app11177983

Cited by 6 publications

(3 citation statements)

References 39 publications

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“…Wan et al [41] proposed a multi-adversarial domain adaptation fault diagnosis model that combines MMD and multiadversarial learning, in which the MMD module calculates the joint metric loss of the distribution discrepancy between domains and between categories. 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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“…They combined the network with the supervised instance-based approach to learn the discriminant characteristics with better intra-class cohesion and inter-class separability. Xu et al [ 95 ] proposed the domain adaptive network model with dual adversarial mechanisms (DAN-DAM), and WD and MMD were used to reduce the difference between the two adversarial mechanisms. Ying et al [ 96 ] proposed an asymmetric adversarial domain adaptive method based on Wasserstein distance.…”

Section: The Research Progress Of Adversarial-based Dtlmentioning

confidence: 99%

Adversarial Deep Transfer Learning in Fault Diagnosis: Progress, Challenges, and Future Prospects

Guo,

Zhang,

Sun

et al. 2023

Sensors

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Deep Transfer Learning (DTL) signifies a novel paradigm in machine learning, merging the superiorities of deep learning in feature representation with the merits of transfer learning in knowledge transference. This synergistic integration propels DTL to the forefront of research and development within the Intelligent Fault Diagnosis (IFD) sphere. While the early DTL paradigms, reliant on fine-tuning, demonstrated effectiveness, they encountered considerable obstacles in complex domains. In response to these challenges, Adversarial Deep Transfer Learning (ADTL) emerged. This review first categorizes ADTL into non-generative and generative models. The former expands upon traditional DTL, focusing on the efficient transference of features and mapping relationships, while the latter employs technologies such as Generative Adversarial Networks (GANs) to facilitate feature transformation. A thorough examination of the recent advancements of ADTL in the IFD field follows. The review concludes by summarizing the current challenges and future directions for DTL in fault diagnosis, including issues such as data imbalance, negative transfer, and adversarial training stability. Through this cohesive analysis, this review aims to offer valuable insights and guidance for the optimization and implementation of ADTL in real-world industrial scenarios.

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“…It is necessary to monitor the health of rotating parts and diagnose faults in complex working conditions because the working environment of high-speed rotating machinery is usually very harsh and complicated [1,2]. However, the artificially extracted features of traditional fault diagnosis methods are usually shallow features.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis

Zeng

et al. 2022

Applied Sciences

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Although some traditional autoencoders and their extensions have been widely used in the research of intelligent fault diagnosis of rotating parts, their feature extraction capabilities are limited without label information. In response to this problem, this research proposes a hierarchical sparse discriminant autoencoder (HSDAE) method for fault diagnosis of rotating components, which is a new semi-supervised autoencoder structure. By considering the sparsity of autoencoders, a hierarchical sparsity strategy was proposed to improve the stacked sparsity autoencoders, and the particle swarm optimization algorithm was used to obtain the optimal sparsity parameters to improve network performance. In order to enhance the classification of the autoencoder, a class aggregation and class separability strategy was used, which is an additional discriminative distance that was added as a penalty term in the loss function to enhance the feature extraction ability of the network. Finally, the reliability of the proposed method was verified on the bearing data set of Case Western Reserve University and the bearing data set of the laboratory test platform. The results of comparison with other methods show that the HSDAE method can enhance the feature extraction ability of the network and has reliability and stability for different data sets.

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Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis

Cited by 6 publications

References 39 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

Adversarial Deep Transfer Learning in Fault Diagnosis: Progress, Challenges, and Future Prospects

A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis

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