Fault Diagnosis of Rotating Machinery based on Domain Adversarial Training of Neural Networks

Di, Yun; Yang, Rui; Huang, Mengjie

doi:10.1109/isie45552.2021.9576238

Cited by 12 publications

(4 citation statements)

References 24 publications

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“…Here this paper compares the performance of the IATN with CNN [61], DANN [62], AMDSA, DASN, ISAE-CSDF [63] and CDGATLN [64] models on the three datasets, and the results are shown in Tables 4-6. In addition, the experiment in this paper is repeated 20 times to eliminate the influence of randomness.…”

Section: Resultsmentioning

confidence: 99%

Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions

Wang,

Ahmed,

Chen

et al. 2024

Applied Sciences

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Bearings are one of the critical components of rotating machinery, and their failure can cause catastrophic consequences. In this regard, previous studies have proposed a variety of intelligent diagnosis methods. Most existing bearing fault diagnosis methods implicitly assume that the training and test sets are from the same distribution. However, in real scenarios, bearings have been working in complex and changeable working environments for a long time. The data during their working processes and the data used for model training cannot meet this condition. This paper proposes an improved adversarial transfer network for fault diagnosis under variable working conditions. Specifically, this paper combines an adversarial transfer network with a short-time Fourier transform to obtain satisfactory results with the lighter network. Then, this paper employs a channel attention module to enhance feature fusion. Moreover, this paper designs a novel domain discrepancy hybrid metric loss to improve model transfer learning performance. Finally, this paper verifies the method’s effectiveness on three datasets, including dual-rotor, a Case Western Reserve University dataset and the Ottawa dataset. The proposed method achieves average accuracy, surpassing other methods, and shows better domain alignment capabilities.

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Section: Resultsmentioning

confidence: 99%

Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions

Wang,

Ahmed,

Chen

et al. 2024

Applied Sciences

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“…Wu et al [ 44 ] proposed a lightweight domain adversarial neural network (LDANN) in which a lightweight feature extractor is constructed. Di et al [ 45 ] propose a method based on cohesion evaluation and DANN, and unlabeled source domain data are also used for the training of domain classifiers. Joint Distribution Adaptation (JDA) Theoretical Background …”

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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“…Domain adaptation (DA), as an active and widely embraced subfield within the realm of transfer learning, has garnered significant attention in its application towards enhancing the diagnostic efficacy of intelligent fault diagnosis techniques, specifically in the context of cross-domain scenarios. Within the domain of intelligent fault diagnosis, two commonly employed methods for domain adaptation have emerged as prominent solutions: robust regularization-based [13][14][15][16][17][18] and domain adversarial-based [19][20][21]. In the first one, variance measures such as the maximum mean difference [22], Wasserstein [23], and CORAL [24] are embedded into the objective function, allowing the network to learn domain-invariant features during the training process.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Marine Diesel Engines under Partial Set and Cross Working Conditions Based on Transfer Learning

Guo,

Zhang

2023

JMSE

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The development of intelligent ships has an urgent demand for intelligent fault diagnosis technology. The working conditions and fault modes of high-power marine diesel engines gradually tend to be diversified and complicated, and the problems of reliability and safety are becoming more and more prominent. There are a lot of working condition data that lack fault labels, and the fault modes are asymmetric among different working conditions, so it is urgent to study effective fault diagnosis methods. Taking a marine diesel engine as the case validation object, we set up cross-condition and partial set fault diagnosis scenarios, proposed transferring knowledge from the source condition to the target condition for the problem of the lack of fault labels in the target condition, and designed a multi-scale and multi-view domain adversarial network (MMDAN) method for experimental validation using 6S50MC-C7 marine diesel engine system operation data. According to the experimental results, the average diagnostic accuracy of this method reached 96.58%, with a short processing time. Furthermore, it exhibits superior diagnostic performance compared to other transfer learning models in the cross-condition partial set transfer task. Additionally, the method proposed in this paper also offers a new approach and reference for the intelligent diagnosis of other equipment in ships.

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Fault Diagnosis of Rotating Machinery based on Domain Adversarial Training of Neural Networks

Cited by 12 publications

References 24 publications

Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions

Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions

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

Fault Diagnosis of Marine Diesel Engines under Partial Set and Cross Working Conditions Based on Transfer Learning

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