An enhanced domain-adversarial neural networks for intelligent cross-domain fault diagnosis of rotating machinery

Zhang, Zhongwei; Shao, Mingyu; Ma, Chicheng; Lv, Zijian; Zhou, Jilei

doi:10.1007/s11071-022-07341-6

Cited by 16 publications

(4 citation statements)

References 36 publications

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“…Jiao et al [37] proposed a hybrid adversarial adaptive intelligence framework for cross-domain fault diagnosis of mechanical equipment, which simultaneously reduces edge release and conditional distribution discrepancy through an adversarial learning strategy. Zhang et al [38] proposed an augmented DANN model for fault diagnosis across loads and validated its effectiveness on bearing and gearbox datasets.…”

Section: Fault Diagnosis Methods Based On Domain Adaptation 221 Metho...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: Fault Diagnosis Methods Based On Domain Adaptation 221 Metho...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 model is constrained by deep CORAL to prevent the degradation of learning caused by asymmetric mapping and adversarial learning. Zhang et al [ 100 ] proposed a deep sparse filtering model as an extractor domain adaptive method for fault features, in order to ensure the generalization ability and robustness of the model. Z-score normalization and CORAL, respectively, help to reduce the impacts of features with large variance and reduce the offset between the two domains.…”

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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“…In the process of bearing operation, once the failure occurs, it will affect industrial production cause economic losses, and may cause safety accidents and endanger human life safety [3, * Author to whom any correspondence should be addressed. 4]. Therefore, timely and accurate bearing fault recognition is crucial to ensure the safe and efficient working of equipment.…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing fault diagnosis based on the fusion of sparse filtering and discriminative domain adaptation method under multi-channel data-driven

Jiao,

Zhang,

et al. 2024

Meas. Sci. Technol.

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Currently, the diagnostic performance of many deep learning algorithms may drop dramatically when the distribution of training data is significantly different from that of the test data. Moreover, the fault diagnosis approaches based on single-channel data may suffer problems such as large precision fluctuation, low reliability, and incomplete expression of fault features. To overcome the above deficiencies, a novel multi-channel data-driven fault recognition method based on the fusion of sparse filtering and discriminative domain adaptation (MSFDDA) is proposed in this article. Firstly, inspired by attention mechanisms and information fusion methods, a spectrum-based weighted multi-channel data fusion strategy (WMCDF) is designed to fully utilize the data collected by sensors to obtain a more comprehensive representation of fault features. Then, the joint probability-based discriminative maximum mean discrepancy algorithm is introduced into the sparse filtering method to strengthen the capability of extracting the domain invariant features. Finally, two bearing datasets are employed to verify the validity of the MSFDDA method, which proved to be superior to other current domain adaptation methods.

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An enhanced domain-adversarial neural networks for intelligent cross-domain fault diagnosis of rotating machinery

Cited by 16 publications

References 36 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

Rolling bearing fault diagnosis based on the fusion of sparse filtering and discriminative domain adaptation method under multi-channel data-driven

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