Rolling Bearing Fault Diagnosis Method Based on Self-Calibrated Coordinate Attention Mechanism and Multi-Scale Convolutional Neural Network Under Small Samples

Xue, Linlin; Lei, Chunli; Jiao, Mengxuan; Li, Jianhua

doi:10.1109/jsen.2023.3260208

Cited by 22 publications

(5 citation statements)

References 31 publications

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“…SP is a method for illustrating changes in the frequency domain over time in time-series data. SP generates a 2D image by employing time and frequency as the two axes and employs color to represent energy density at their intersections [ 28 , 29 ]. The widely used algorithm is the short-time Fourier transform (STFT), which divides the time-series data into small sections of a specific size and calculates the Fourier transform for each section.…”

Section: Techniques Of Data Generation and Image Encodingmentioning

confidence: 99%

“…Recent research has compared traditional 1D time-series data approaches with image encoding techniques, presenting findings that substantiate the superior efficacy of image encoding methods [ 17 , 18 ]. Representative image encoding techniques include recurrence plot (RP) [ 19 , 20 , 21 , 22 , 23 ], Gramian angular field (GAF) [ 14 , 24 , 25 , 26 , 27 ], Markov transition field (MTF) [ 28 , 29 , 30 ], spectrogram (SP) [ 31 , 32 ], and scalogram (SC) [ 33 , 34 ]. These image encoding techniques have recently been applied in research that converts time-series data from vibration and current signals, collected for diagnosing faults in robots and various machinery (such as bearings, gearboxes, rotating machinery, complex distribution networks, ventilation, and air conditioning systems), into images for various convolutional neural network (CNN) models.…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis Method for Human Coexistence Robots Based on Convolutional Neural Networks Using Time-Series Data Generation and Image Encoding

Choi,

Park,

et al. 2023

Sensors

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This paper proposes fault diagnosis methods aimed at proactively preventing potential safety issues in robot systems, particularly human coexistence robots (HCRs) used in industrial environments. The data were collected from durability tests of the driving module for HCRs, gathering time-series vibration data until the module failed. In this study, to apply classification methods in the absence of post-failure data, the initial 50% of the collected data were designated as the normal section, and the data from the 10 h immediately preceding the failure were selected as the fault section. To generate additional data for the limited fault dataset, the Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) model was utilized and residual connections were added to the generator to maintain the basic structure while preventing the loss of key features of the data. Considering that the performance of image encoding techniques varies depending on the dataset type, this study applied and compared five image encoding methods and four CNN models to facilitate the selection of the most suitable algorithm. The time-series data were converted into image data using image encoding techniques including recurrence plot, Gramian angular field, Markov transition field, spectrogram, and scalogram. These images were then applied to CNN models, including VGGNet, GoogleNet, ResNet, and DenseNet, to calculate the accuracy of fault diagnosis and compare the performance of each model. The experimental results demonstrated significant improvements in diagnostic accuracy when employing the WGAN-GP model to generate fault data, and among the image encoding techniques and convolutional neural network models, spectrogram and DenseNet exhibited superior performance, respectively.

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Section: Techniques Of Data Generation and Image Encodingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Human Coexistence Robots Based on Convolutional Neural Networks Using Time-Series Data Generation and Image Encoding

Choi,

Park,

et al. 2023

Sensors

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“…The rolling bearing, as a fundamental component of mechanical equipment, plays a crucial role in load support and transmission. Studies have shown that more than 40% of mechanical failures are related to the failure of rolling bearings [1]. These failures can significantly affect equipment productivity and lifespan, potentially leading to severe damage, hazardous accidents, and substantial financial losses [2].…”

Section: Introductionmentioning

confidence: 99%

Multi-scale quaternion CNN and BiGRU with cross self-attention feature fusion for fault diagnosis of bearing

Liu,

Zhang,

Tan

et al. 2024

Meas. Sci. Technol.

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In recent years, deep learning has led to significant advances in bearing fault diagnosis (FD). Most techniques aim to achieve greater accuracy. However, they are sensitive to noise and lack robustness, resulting in insufficient domain adaptation and anti-noise ability. The comparison of studies reveals that giving equal attention to all features does not differentiate their significance. In this work, we propose a novel FD model by integrating multi-scale quaternion convolutional neural network (MQCNN), bidirectional gated recurrent unit (BiGRU), and cross self-attention feature fusion (CSAFF). We have developed innovative designs in two modules, namely MQCNN and CSAFF. Firstly, MQCNN applies quaternion convolution to multi-scale architecture for the first time, aiming to extract the rich hidden features of the original signal from multiple scales. Then, the extracted multi-scale information is input into CSAFF for feature fusion, where CSAFF innovatively incorporates cross self-attention mechanism to enhance discriminative interaction representation within features. Finally, BiGRU captures temporal dependencies while a softmax layer is employed for fault classification, achieving accurate FD. To assess the efficacy of our approach, we experiment on three public datasets (CWRU, MFPT, and Ottawa) and compare it with other excellent methods. The results confirm its state-of-the-art, which the average accuracies can achieve up to 99.99%, 100%, and 99.21% on CWRU, MFPT, and Ottawa datasets. Moreover, we perform practical tests and ablation experiments to validate the efficacy and robustness of the proposed approach.

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“…Liu et al [29] proposed a fault diagnosis method based on a multi-scale fusion attention CNN (MSFACNN) to improve the fault diagnostic accuracy of aero engine bearings using small samples. In addition, other studies [30,31] combined attention mechanisms to extract richer depth features under small sample conditions. The authors in [32] combined five different neural network structures for experiments, while those in [33] combined multiple regression and fuzzy neural networks for small sample prediction.…”

Section: Introductionmentioning

confidence: 99%

A Bearing Fault Diagnosis Method under Small Sample Conditions Based on the Fractional Order Siamese Deep Residual Shrinkage Network

Li,

Wu,

Luo

et al. 2024

Fractal Fract

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A bearing fault is one of the major causes of rotating machinery faults. However, in real industrial scenarios, the harsh and complex environment makes it very difficult to collect sufficient fault data. Due to this limitation, most of the current methods cannot accurately identify the fault type in cases with limited data, so timely maintenance cannot be conducted. In order to solve this problem, a bearing fault diagnosis method based on the fractional order Siamese deep residual shrinkage network (FO-SDRSN) is proposed in this paper. After data collection, all kinds of vibration data are first converted into two-dimensional time series feature maps, and these feature maps are divided into the same or different types of fault sample pairs. Then, a Siamese network based on the deep residual shrinkage network (DRSN) is used to extract the features of the fault sample pairs, and the fault type is determined according to the features. After that, the contrastive loss function and diagnostic loss function of the sample pairs are combined, and the network parameters are continuously optimized using the fractional order momentum gradient descent method to reduce the loss function. This improves the accuracy of fault diagnosis with a small sample training dataset. Finally, four small sample datasets are used to verify the effectiveness of the proposed method. The results show that the FO-SDRSN method is superior to other advanced methods in terms of training accuracy and stability under small sample conditions.

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Rolling Bearing Fault Diagnosis Method Based on Self-Calibrated Coordinate Attention Mechanism and Multi-Scale Convolutional Neural Network Under Small Samples

Cited by 22 publications

References 31 publications

Fault Diagnosis Method for Human Coexistence Robots Based on Convolutional Neural Networks Using Time-Series Data Generation and Image Encoding

Fault Diagnosis Method for Human Coexistence Robots Based on Convolutional Neural Networks Using Time-Series Data Generation and Image Encoding

Multi-scale quaternion CNN and BiGRU with cross self-attention feature fusion for fault diagnosis of bearing

A Bearing Fault Diagnosis Method under Small Sample Conditions Based on the Fractional Order Siamese Deep Residual Shrinkage Network

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