Deep balanced domain adaptation neural networks for fault diagnosis of planetary gearboxes with limited labeled data

Li, Qikang; Tang, Baoping; Deng, Lei; Wu, Yanling; Wang, Yi

doi:10.1016/j.measurement.2020.107570

Cited by 61 publications

(20 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In comparison to the accuracy of 99.50% used by conventional CNN, this method achieved 99.75% under compound situations, which shows a slight advantage over other DLbased approaches. With the purpose of tackling the discrepancy of the marginal and conditional probability distribution, multi-layer balanced domain adaptation was used in the training process followed by the feature extraction of traditional CNN [98]. In view of the limitation of the marked data, the domain adaptation step was brought into the network and arranged behind the feature extraction layer and before the output layer.…”

Section: B Cnn-based Fault Diagnosis For Gear and Gearboxmentioning

confidence: 99%

“…Visualization of the two domains in each full connection layer with different methods. MMD represents maximum mean discrepancy, N-DANN represents a domain adaptation neural network with no MMD method applied, M-DANN represents a domain adaptation neural network with multi-MMD method, and B-DANN represents the deep domain adaptation method with multi-MMD to balance the discrepancy of two distributions[98].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

2020

View full text Add to dashboard Cite

Rotating machinery is of vital importance in the field of engineering, including aviation and navigation. Its failure will lead to severe loss to personnel safety and the stability of the equipment system. It is a long way to investigate the relevant fault diagnosis method, especially the intelligent fault diagnosis method on the basis of deep learning. In consideration of the limitations of traditional fault diagnosis approaches based on shallow layer network structure, the methods based on deep neural network (DNN) are worthy of thorough exploration. As a common DNN with special structure, deep convolutional neural network is of great concern in intelligent fault diagnosis due to its advantages in processing nonlinear problems. This review will play an emphasis on convolutional neural network (CNN). The basic structure and principle are introduced. The applications of CNN-based fault diagnosis method in rotating machinery are summarized and analyzed. Furthermore, the diagnosis performance and potential mechanism from different CNN methods are discussed. In the end, this review is highlighted on the challenges and the potential key points in research on novel intelligent fault diagnosis strategies. The corresponding analysis and discussion will provide some references and lay the foundation for the investigation in related fields. INDEX TERMS Deep learning, convolutional neural network, intelligent fault diagnosis, rotating machinery.

show abstract

Section: B Cnn-based Fault Diagnosis For Gear and Gearboxmentioning

confidence: 99%

mentioning

confidence: 99%

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

2020

View full text Add to dashboard Cite

show abstract

“…Hang et al [37] applied a two-step clustering algorithm and principal component analysis to improve classification performance in the case of unbalanced highdimensional data. Li et al [38] proposed a deep, balanced domain adaptation neural network, which achieved satisfactory results with limited labeled data. Duan et al [39] proposed a novel data description support vector based on deep learning for unbalanced datasets.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Matching Network for Fault Diagnosis under Different Working Conditions with Limited Data

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Intelligent fault diagnosis methods based on deep learning have achieved much progress in recent years. However, there are two major factors causing serious degradation of the performance of these algorithms in real industrial applications, i.e., limited labeled training data and complex working conditions. To solve these problems, this study proposed a domain generalization-based hybrid matching network utilizing a matching network to diagnose the faults using features encoded by an autoencoder. The main idea was to regularize the feature extractor of the network with an autoencoder in order to reduce the risk of overfitting with limited training samples. In addition, a training strategy using dropout with random changing rates on inputs was implemented to enhance the model’s generalization on unseen domains. The proposed method was validated on two different datasets containing artificial and real faults. The results showed that considerable performance was achieved by the proposed method under cross-domain tasks with limited training samples.

show abstract

“…A novel method termed meta-learning fault diagnosis framework was proposed by Li et al [ 22 ] and performed excellently under complex working conditions. Li et al [ 23 ] designed a deep balanced domain adaptation neural network achieving exciting results using limited labeled training data. Hang et al [ 24 ] used principal component analysis and a two-step clustering algorithm to develop performance in a high-dimensional unbalanced training dataset.…”

Section: Introductionmentioning

confidence: 99%

A Siamese Vision Transformer for Bearings Fault Diagnosis

Bai

et al. 2022

Micromachines

View full text Add to dashboard Cite

Fault diagnosis methods based on deep learning have progressed greatly in recent years. However, the limited training data and complex work conditions still restrict the application of these intelligent methods. This paper proposes an intelligent bearing fault diagnosis method, i.e., Siamese Vision Transformer, suiting limited training data and complex work conditions. The Siamese Vision Transformer, combining Siamese network and Vision Transformer, is designed to efficiently extract the feature vectors of input samples in high-level space and complete the classification of the fault. In addition, a new loss function combining the Kullback-Liebler divergence both directions is proposed to improve the performance of the proposed model. Furthermore, a new training strategy termed random mask is designed to enhance input data diversity. A comparative test is conducted on the Case Western Reserve University bearing dataset and Paderborn dataset and our method achieves reasonably high accuracy with limited data and satisfactory generation capability for cross-domain tasks.

show abstract

Deep balanced domain adaptation neural networks for fault diagnosis of planetary gearboxes with limited labeled data

Cited by 61 publications

References 25 publications

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

A Hybrid Matching Network for Fault Diagnosis under Different Working Conditions with Limited Data

A Siamese Vision Transformer for Bearings Fault Diagnosis

Contact Info

Product

Resources

About