Deep domain adaptation with adversarial idea and coral alignment for transfer fault diagnosis of rolling bearing

Li, Ranran; Li, Shunming; Xu, Kun; Lu, Jiming; Teng, Guangrong; Du, Jun

doi:10.1088/1361-6501/abe163

Cited by 26 publications

(17 citation statements)

References 39 publications

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“…CNNs are widely used in the field of fault diagnosis. 24,25 The deep CNNs can extract the deep features of the input signal through the deep network, and can make effective judgments on the extracted deep features and identify the fault type. Deep CNNs are mainly composed of convolutional layers, activation layers, pooling layers, and fully connected layers.…”

Section: Convolutional Neural Network and Deconvolutional Neural Networkmentioning

confidence: 99%

Dual generative adversarial networks combining conditional assistance and feature enhancement for imbalanced fault diagnosis

et al. 2023

Structural Health Monitoring

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The dataset in the application scenario of existing fault diagnosis methods is often balanced, while the data collected under actual working conditions are often imbalanced. Directly applying existing fault diagnosis methods to this scenario will lead to poor diagnosis effect. In view of the above problems, we proposed a method called dual generative adversarial networks (DGANs) combining conditional assistance and feature enhancement. The method uses data augmentation as a basic strategy to supplement imbalanced datasets by generating high-quality data. Firstly, a new generator is designed to build the basic framework by sharing the dual-branch deconvolutional neural networks, and combining the label auxiliary information and the coral distance loss function to ensure the diversity of generated samples. Secondly, a new discriminator was designed, which is based on deep convolutional neural networks and embedded with auxiliary classifiers, further expanding the function of the discriminator. Thirdly, the self-attention module is introduced into both the generator and the discriminator to enhance deep feature learning and improve the quality of generated samples; finally, the proposed method is experimentally validated on datasets of two different testbeds. The experimental results show that the proposed method can generate fake samples with rich diversity and high quality, using these samples to supplement the imbalanced dataset, the effect of imbalanced fault diagnosis has been substantially improved. This method can be used to solve the problem of fault diagnosis in the case of sample imbalance, which often exists in actual working conditions.

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Section: Convolutional Neural Network and Deconvolutional Neural Networkmentioning

confidence: 99%

Dual generative adversarial networks combining conditional assistance and feature enhancement for imbalanced fault diagnosis

et al. 2023

Structural Health Monitoring

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“…Nowadays, the difficult problems of intelligent diagnosis have gradually started to focus on the fault diagnosis in variable speed [12], unbalanced sample [13] and noise environment [14]. As the problem of variable speed transfer diagnosis is a basic problem to be solved in almost all equipment, transfer fault diagnosis [15,16] has become a hot research topic at present.…”

Section: Introductionmentioning

confidence: 99%

Siamese Distinguishing Features Attentional Enhancement Transfer Fault Diagnosis Method for Variable Rotational Speed

Xu¹,

et al. 2024

IEEE Access

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Transfer learning is widely used in artificial intelligence fault diagnosis field because it can solve the problem of label missing in rotating parts at varying speeds. However, the domain adaptive method in transfer learning is not suitable for real transfer fault diagnosis scenarios, and the adaptive enhancement of fault characteristics is not realized in the transfer process. To solve these thorny problems, a novel method called Siamese Distinguishing features Attentional Enhancement Transfer fault diagnosis (SDAET) has been proposed. The body of SDAET model adopts the dual-branch convolutional neural network architecture with shared weights. It mainly uses the contrast loss function in the siamese feature contrast network to extract the domain invariant features at two rotational speeds, and then applies it to the transfer diagnosis at other rotational speeds, so as to meet the more real transfer diagnosis scenarios. The distinguishing features attentional enhancement network is designed to adaptively enhance the differentiated domain invariant features at different rotational speeds. Furthermore, various feature visualization techniques are used to further explain the features learned from the black box neural network. The diagnostic results on two kinds of test datasets show that the proposed model has higher diagnostic accuracy. The technique of siamese distinguishing features attentional enhancement provides a new and better way to solve the transfer diagnosis problem.

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“…As an important component of mechanical transmission device, rolling bearing has a relatively high frequency of failure [1,2]. Therefore, fault monitoring and early warning diagnosis of rolling bearings is of great significance to ensure the safe and reliable operation of mechanical equipment and avoid safety accidents [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Symmetric Stacked Autoencoder for Adversarial Domain Adaptation Under Variable Speed

et al. 2022

IEEE Access

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At present, most of the fault diagnosis methods with extensive research and good diagnostic effect are based on the premise that the sample distribution is consistent. However, in reality, the sample distribution of rotating machinery is inconsistent due to variable working conditions, and most of the fault diagnosis algorithms have poor diagnostic effects or even invalid. To dispose the above problems, a novel symmetric stacked autoencoder (NSSAE) for adversarial domain adaptation is proposed. Firstly, the symmetric stacked autoencoder network with shared weights is used as the feature extractor to extract features which can better express the original signal. Secondly, adding domain discriminator that constituting adversarial with feature extractor to enhance the ability of feature extractor to extract domain invariant features, thus confusing the domain discriminator and making it unable to correctly distinguish the features of the two domains. Finally, to assist the adversarial training, the maximum mean discrepancy (MMD) is added to the last layer of the feature extractor to align the features of the two domains in the high-dimensional space. The experimental results show that, under the condition of variable speed, the NSSAE model can extract domain invariant features to achieve the transfer between domains, and the transfer diagnosis accuracy is high and the stability is strong.

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Deep domain adaptation with adversarial idea and coral alignment for transfer fault diagnosis of rolling bearing

Cited by 26 publications

References 39 publications

Dual generative adversarial networks combining conditional assistance and feature enhancement for imbalanced fault diagnosis

Dual generative adversarial networks combining conditional assistance and feature enhancement for imbalanced fault diagnosis

Siamese Distinguishing Features Attentional Enhancement Transfer Fault Diagnosis Method for Variable Rotational Speed

A Novel Symmetric Stacked Autoencoder for Adversarial Domain Adaptation Under Variable Speed

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