A Fault Diagnostic Scheme Based on Capsule Network for Rolling Bearing under Different Rotational Speeds

Fu, Pingqing; Zhang, Mian; Wang, Kesheng

doi:10.3390/s20071841

Cited by 9 publications

(8 citation statements)

References 26 publications

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“…Li et al [18] introduced a dual convolution-capsule network containing dual groups of convolutional layers, pooling layers and capsule structures for fault identification in the case of limited data. Zhu et al [19] presented a CapsNet with strong generalization that introduced an inception block and a regression branch. Li et al [20] fused vertical and horizontal vibration signals into a capsule network for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A multi-scale spatial–temporal capsule network based on sequence encoding for bearing fault diagnosis

Wang,

Chen

2024

Complex Intell. Syst.

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The Capsule Network (CapsNet) has been shown to have significant advantages in improving the accuracy of bearing fault identification. Nevertheless, the CapsNet faces challenges in identifying the type of bearing fault under nonstationary and noisy conditions. These challenges arise from the distinctive nature of its dynamic routing algorithm and the use of fixed single-scale kernels. To address these challenges, a multi-scale spatial–temporal capsule network (MSCN) based on sequence encoding is proposed for bearing fault identification under nonstationary and noisy environments. A spatial–temporal sequence encoding module focuses on feature correlations at various times and positions. Dilated convolution-based multiscale capsule layer (MCaps) is designed to capture spatial–temporal features at different scales. MCaps establishes connections between various layers, enhancing the comprehension and interpretation of spatial–temporal features. Furthermore, the Bhattacharyya coefficient is introduced into the dynamic routing to compare the similarity between capsules. The validity of the model is verified through comparative experiments, and the results show that MSCN has significant advantages over traditional methods.

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Section: Introductionmentioning

confidence: 99%

A multi-scale spatial–temporal capsule network based on sequence encoding for bearing fault diagnosis

Wang,

Chen

2024

Complex Intell. Syst.

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“…Due to these abilities, it is theoretically possible to perform the diagnostic analysis of faults with a small number of samples. Owing to these characteristics, Capsnet and its variants have been developed for the application to the fault diagnosis of bearings [ 23 , 24 , 25 , 26 , 27 ]. For example, the Bi-LSTM and Capsule network with CNN are used effectively to diagnose bearing faults with insufficient fault data samples [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…Owing to these characteristics, Capsnet and its variants have been developed for the application to the fault diagnosis of bearings [ 23 , 24 , 25 , 26 , 27 ]. For example, the Bi-LSTM and Capsule network with CNN are used effectively to diagnose bearing faults with insufficient fault data samples [ 24 ]. Furthermore, a novel capsule network with an inception block and a regression branch has been proposed for the diagnosis of bearing faults with high accuracy and good generalization [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Capsule Network with an Attention Mechanism for the Fault Diagnosis of Bearings from Imbalanced Data Samples

Zhang

Lee

et al. 2022

Sensors

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In order to solve the problem of imbalanced and noisy data samples for the fault diagnosis of rolling bearings, a novel ensemble capsule network (Capsnet) with a convolutional block attention module (CBAM) that is based on a weighted majority voting method is proposed in this study. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method was used to decompose the raw vibration signal into different IMF signals, which are noise reduction signals. Secondly, the IMF signals were input into the Capsnet with CBAM in order to diagnose the fault category preliminarily. Finally, the weighted majority voting method was utilized so as to fuse all of the preliminary diagnosis results in order to obtain the final diagnostic decision. In order to verify the effectiveness of the proposed ensemble of Capsnet with CBAM, this method was applied to the fault diagnosis of rolling bearings with imbalanced and different SNR data samples. The diagnostic results show that the proposed diagnostic method can achieve higher levels of accuracy than other methods, such as single CNN, single Capsnet, ensemble CNN and an ensemble capsule network without CBAM and that it has stronger immunity to noise than an ensemble capsule network without CBAM.

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“…Wen et al [ 17 ] proposed a new deep transfer model-based diagnosis approach, in which, the feature extraction task is fulfilled by a sparse auto-encoder network, and the inconsistency between the distributions of testing and training set is minimized by the MMD, thereby the domain adaptation process is accomplished. Li et al [ 18 ] presented an end-to-end scheme that combines bidirectional signals and capsule networks to input horizontal and vertical vibration signals into the neural network. Using the proposed scheme, domain-invariant features can be learned from training samples collected under variable working conditions.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Capsule Attention Network and Joint Distributed Optimal Transport for Bearing Fault Diagnosis under Different Working Loads

Sun

Yuan

et al. 2021

Sensors

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In recent years, intelligent fault diagnosis methods based on deep learning have developed rapidly. However, most of the existing work performs well under the assumption that training and testing samples are collected from the same distribution, and the performance drops sharply when the data distribution changes. For rolling bearings, the data distribution will change when the load and speed change. In this article, to improve fault diagnosis accuracy and anti-noise ability under different working loads, a transfer learning method based on multi-scale capsule attention network and joint distributed optimal transport (MSCAN-JDOT) is proposed for bearing fault diagnosis under different loads. Because multi-scale capsule attention networks can improve feature expression ability and anti-noise performance, the fault data can be better expressed. Using the domain adaptation ability of joint distribution optimal transport, the feature distribution of fault data under different loads is aligned, and domain-invariant features are learned. Through experiments that investigate bearings fault diagnosis under different loads, the effectiveness of MSCAN-JDOT is verified; the fault diagnosis accuracy is higher than that of other methods. In addition, fault diagnosis experiment is carried out in different noise environments to demonstrate MSCAN-JDOT, which achieves a better anti-noise ability than other transfer learning methods.

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A Fault Diagnostic Scheme Based on Capsule Network for Rolling Bearing under Different Rotational Speeds

Cited by 9 publications

References 26 publications

A multi-scale spatial–temporal capsule network based on sequence encoding for bearing fault diagnosis

A multi-scale spatial–temporal capsule network based on sequence encoding for bearing fault diagnosis

Ensemble Capsule Network with an Attention Mechanism for the Fault Diagnosis of Bearings from Imbalanced Data Samples

Multi-Scale Capsule Attention Network and Joint Distributed Optimal Transport for Bearing Fault Diagnosis under Different Working Loads

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