Multi-Fault Classification and Diagnosis of Rolling Bearing Based on Improved Convolution Neural Network

Zhang, Xiong; Li, Jialu; Wu, Wenbo; Dong, Fan; Wan, Shuting

doi:10.3390/e25050737

Cited by 8 publications

(4 citation statements)

References 26 publications

(31 reference statements)

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“…Zhang et al [19] applied 1D-CNN to construct a multi-scale residual attention network, which can learn multi-scale features of signals in a highnoise environment. Zhang et al [20] designed an improved three-layer CNN structure to realize the fault diagnosis of bearing with multiple operating conditions. However, all the above studies are based on the assumption that the sample data is labeled and that the samples used for model training and the samples to be tested obey the same distribution.…”

Section: Introductionmentioning

confidence: 99%

Novel Adversarial Unsupervised Subdomain Adaption Multi-Channel Deep Convolutional Network for Cross-Operating Fault Diagnosis of Rolling Bearings

Zhang,

Huo,

Liu

et al. 2024

IEEE Access

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Rolling bearings in production practice usually serve in a healthy state. Some fault state labels are scarce or even no labels, resulting in unbalanced data categories. Meanwhile, frequent working condition switching results in significant differences in data distribution among working conditions, and labeled data in some working states cannot be fully utilized. To deal with the challenge of low fault identification accuracy caused by these practical factors, this paper proposed a novel adversarial unsupervised subdomain adaption multi-channel deep convolutional network (ASMDCN). Firstly, a parallel three-channel depth feature extraction module is built, and a multi-scale convolution kernel is used to fully extract the rich features of vibration signals under various working conditions. Secondly, a novel loss function is designed to adequately consider the classification difficulty of samples and the degree of class imbalance. Finally, the adversarial training strategy is used to force the feature extractor to extract the domain invariant features, and the Local Maximum Mean discrepancy (LMMD) is used to align the global and related subdomains of the source and target domains. The experimental results show that the designed feature extraction can fully extract the domain-invariant features of the rolling bearings under different working conditions. Under the proposed objective function optimization, the network model can fully align the features of multi-source and singletarget domain under unbalanced data and has strong generalization performance.

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Section: Introductionmentioning

confidence: 99%

Novel Adversarial Unsupervised Subdomain Adaption Multi-Channel Deep Convolutional Network for Cross-Operating Fault Diagnosis of Rolling Bearings

Zhang,

Huo,

Liu

et al. 2024

IEEE Access

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“…In addition, there is an increasing focus on using deep learning for more efficient and robust fault diagnosis under various conditions. Techniques like the deep focus parallel convolutional neural network (DFPCN) have been introduced to address imbalanced machine fault diagnosis [17], and simplified CNN structures have been proposed for more effective rolling bearing fault diagnosis [18]. Multisensor approaches using 2D deep learning frameworks are also being explored for distributed bearing fault detection [19].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Input Convolutional Neural Network Model for Electric Motor Mechanical Fault Classification Using Multiple Image Transformation and Merging Methods

Bae,

Lee

2024

Machines

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This paper addresses the critical issue of fault detection and prediction in electric motor machinery, a prevalent challenge in industrial applications. Faults in these machines, stemming from mechanical or electrical issues, often lead to performance degradation or malfunctions, manifesting as abnormal signals in vibrations or currents. Our research focuses on enhancing the accuracy of fault classification in electric motor facilities, employing innovative image transformation methods—recurrence plots (RPs), the Gramian angular summation field (GASF), and the Gramian angular difference field (GADF)—in conjunction with a multi-input convolutional neural network (CNN) model. We conducted comprehensive experiments using datasets encompassing four types of machinery components: bearings, belts, shafts, and rotors. The results reveal that our multi-input CNN model exhibits exceptional performance in fault classification across all machinery types, significantly outperforming traditional single-input models. This study not only demonstrates the efficacy of advanced image transformation techniques in fault detection but also underscores the potential of multi-input CNN models in industrial fault diagnosis, paving the way for more reliable and efficient monitoring of electric motor machinery.

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“…Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have significantly enhanced fault diagnosis capabilities by automating feature extraction from raw data [21][22][23]. CNNs excel in learning hierarchical representations, which are vital for distinguishing fault features from noise [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder

Lu,

Zhou,

2024

Electronics

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Vibration signal analysis is regarded as a fundamental approach in diagnosing faults in rolling bearings, and recent advancements have shown notable progress in this domain. However, the presence of substantial background noise often results in the masking of these fault signals, posing a significant challenge for researchers. In response, an adaptive denoising autoencoder (ADAE) approach is proposed in this paper. The data representations are learned by the encoder through convolutional layers, while the data reconstruction is performed by the decoder using deconvolutional layers. Both the encoder and decoder incorporate adaptive shrinkage units to simulate denoising functions, effectively removing interfering information while preserving sensitive fault features. Additionally, dropout regularization is applied to sparsify the network and prevent overfitting, thereby enhancing the overall expressive power of the model. To further enhance ADAE’s noise resistance, shortcut connections are added. Evaluation using publicly available datasets under scenarios with known and unknown noise demonstrates that ADAE effectively enhances the signal-to-noise ratio in strongly noisy backgrounds, facilitating accurate diagnosis of faults in rolling bearings.

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Multi-Fault Classification and Diagnosis of Rolling Bearing Based on Improved Convolution Neural Network

Cited by 8 publications

References 26 publications

Novel Adversarial Unsupervised Subdomain Adaption Multi-Channel Deep Convolutional Network for Cross-Operating Fault Diagnosis of Rolling Bearings

Novel Adversarial Unsupervised Subdomain Adaption Multi-Channel Deep Convolutional Network for Cross-Operating Fault Diagnosis of Rolling Bearings

A Multi-Input Convolutional Neural Network Model for Electric Motor Mechanical Fault Classification Using Multiple Image Transformation and Merging Methods

Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder

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