A train bearing imbalanced fault diagnosis method based on extended CCR and multi-scale feature fusion network

He, Changfu; He, Deqiang; Wei, Zexian; Xu, Kai; Chen, Yanjun; Shan, Sheng

doi:10.1007/s11071-024-09733-2

Nonlinear Dyn

2024

DOI: 10.1007/s11071-024-09733-2

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A train bearing imbalanced fault diagnosis method based on extended CCR and multi-scale feature fusion network

Changfu He,

Deqiang He,

Zexian Wei

et al.

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Intelligent mechanical fault diagnosis using multiscale residual network and multisensor fusion

Guo,

Yu,

Zhang

et al. 2024

Meas. Sci. Technol.

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Mechanical faults in manufacturing systems need to be diagnosed accurately to ensure safety and cost savings. With the development of sensor technologies, data from multiple sensors is frequently utilized to assess the health of intricate industrial systems. In such cases, it is necessary to study the multisensor data based intelligent mechanical fault diagnosis method. First, the multi-sensor data is converted into grey images and then fused into a three-channel red-green-blue (RGB) image. Then, a multiscale with residual convolution module is proposed, which can extract multiscale deep features of the complex raw signal. Additionally, an attention module for channel and spatial attention is introduced to adaptively adjust the feature response values of each scale. Two datasets and a specific engineering application are used to validate the superiority of the network. The results show that the multi-sensor multi-scale residual network outperforms other fault diagnosis networks in terms of fault identification accuracy, diagnostic efficiency, and applicability.

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Intelligent mechanical fault diagnosis using multiscale residual network and multisensor fusion

Guo,

Yu,

Zhang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

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A train bearing imbalanced fault diagnosis method based on extended CCR and multi-scale feature fusion network

Cited by 1 publication

References 54 publications

Intelligent mechanical fault diagnosis using multiscale residual network and multisensor fusion

Intelligent mechanical fault diagnosis using multiscale residual network and multisensor fusion

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