Multiple Working Condition Bearing Fault Diagnosis Method Based on Channel Segmentation Improved Residual Network

Jiang, Yuanyuan; Xie, Jinyang; Lv, Meng; Jia, Hanguang

doi:10.3390/electronics12010145

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…[42], used GAF and MTF to convert the vibration signal into a two-dimensional image, which was then input into a Capsule Network for classification. Googlenet, Resnet34, Resnet50, and Alexnet were obtained from [43]. Table 5 shows in detail the detection results of several image encoding methods and neural networks.…”

Section: Comparison Of the Number Of Neural Network Parametersmentioning

confidence: 99%

Multiscale permutation entropy gray image coding method and its application in bearing fault diagnosis

She,

Ai,

et al. 2024

Eng. Res. Express

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Bearing fault diagnosis is of great significance to the normal operation of machinery, and its performance and life span directly affect the operational efficiency and safety of the whole equipment. For existing image coding methods which detecting the bearing fault with a large number of training samples and complex neural networks to achieve the desired detection performance, the Multiscale Permutation Entropy Gray Image Coding (MPEGIC) method is proposed. In order to fully extract the feature information of the time series signal, this paper uses the Multiscale Permutation Entropy (MPE) method to construct a new image coding method by calculating the alignment information of the time series to reflect the complexity and randomness of the time series at different scales, and dividing the obtained feature matrix and mapping it to the gray-scale image domain. And it is experimentally verified by Case Western Reserve University (CWRU) bearing dataset and self-made rotor experimental platform bearing dataset. The results show that the method in this paper effectively reduces the number of training samples and the number of model parameters, and maintains a better detection performance even in a strong noise background.

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Section: Comparison Of the Number Of Neural Network Parametersmentioning

confidence: 99%

Multiscale permutation entropy gray image coding method and its application in bearing fault diagnosis

She,

Ai,

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

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Fault Diagnosis for Rolling Bearings Based on Multiscale Feature Fusion Deep Residual Networks

Shi

Zhu

2023

Electronics

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Deep learning, due to its excellent feature-adaptive capture ability, has been widely utilized in the fault diagnosis field. However, there are two common problems in deep-learning-based fault diagnosis methods: (1) many researchers attempt to deepen the layers of deep learning models for higher diagnostic accuracy, but degradation problems of deep learning models often occur; and (2) the use of multiscale features can easily be ignored, which makes the extracted data features lack diversity. To deal with these problems, a novel multiscale feature fusion deep residual network is proposed in this paper for the fault diagnosis of rolling bearings, one which contains multiple multiscale feature fusion blocks and a multiscale pooling layer. The multiple multiscale feature fusion block is designed to automatically extract the multiscale features from raw signals, and further compress them for higher dimensional feature mapping. The multiscale pooling layer is constructed to fuse the extracted multiscale feature mapping. Two famous rolling bearing datasets are adopted to evaluate the diagnostic performance of the proposed model. The comparison results show that the diagnostic performance of the proposed model is superior to not only several popular models, but also other advanced methods in the literature.

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Multiple Working Condition Bearing Fault Diagnosis Method Based on Channel Segmentation Improved Residual Network

Cited by 2 publications

References 13 publications

Multiscale permutation entropy gray image coding method and its application in bearing fault diagnosis

Multiscale permutation entropy gray image coding method and its application in bearing fault diagnosis

Fault Diagnosis for Rolling Bearings Based on Multiscale Feature Fusion Deep Residual Networks

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